When numbers remain silent: the protest potential of Kazakhstan’s youth amid social tension

Introduction: We analyze 2019–2024 dynamics of protes-related attitudes among Kazakhstani youth using secondary sources. The study asks: Why did standard surveys fail to register latent youth discontent prior to January 2022, and which survey‑adjacent indicators can improve early‑warning in settings with measurement and disclosure constraints?

Methods: We rely exclusively on published data and prior analyses. Latent protest potential is operationalized via three proxies: (1) acceptance of unsanctioned protest, (2) institutional trust, and (3) digital embeddedness. We synthesize national survey time series, youth subsamples, and open secondary indicators.

Results: We identify a Kazakhstan‑specific “shock–trust–network” activation configuration (fuel‑price shock + trust downswing + decentralized coordination). Attitudinal openness to protest rose in 2022–2023 and moderated in 2024. Conversion from attitudes to on‑street participation appears constrained by perceived regulatory risks, social stigma, and economic vulnerability.

Discussion: Standard survey instruments undercount sensitive attitudes due to question‑wording effects, social desirability, and frame coverage. Survey‑adjacent indicators that jointly track shocks, trust dynamics, and network capacity enhance early‑warning by monitoring preconditions rather than forecasting discrete events. With calibration, the framework is transferable to other contexts where conventional surveys have limited sensitivity on contentious topics.

1 Introduction

The study of protest sentiments in society based on sociological surveys is an essential analytical instrument for examining social values, public attitudes, and levels of social tension. Such surveys not only record the degree of public dissatisfaction with existing social, economic, and political conditions but also identify the mechanisms through which this dissatisfaction develops and examine how it relates to dominant cultural orientations and social institutions. Protest behavior serves as an indicator of social alienation and reveals the depth of the gap between citizens’ expectations and the actual capacity of the state and society to meet their needs (Jasper, 2014).

Protest behavior can be seen as a response to systemic imbalances: the wider the gap between public expectations and the real socioeconomic environment, the greater the likelihood of escalating protest sentiments (Vanhuysse, 2006). If state institutions prove unable to address societal challenges effectively, protest sentiments become a means of articulating collective discontent. These sentiments also function as a critical indicator of the effectiveness of social policy and the quality of communication between the state and its citizens. A rise in protest activity indicates the need to reconsider how authorities engage with the population, as well as the need to establish more equitable and inclusive institutions.

Particular attention in analyzing protest movements should be paid to young people, as youth cohorts are early responders to shifts in opportunity structures and digital coordination; we therefore treat their attitudes as a leading indicator of potential mobilization (Pickard, 2019; Junisbai and Junisbai, 2020). Consistent with cross-national research, we avoid metaphorical descriptors and instead operationalize youth’s role via acceptance of contentious action, institutional trust, and digital embeddedness (see Section 5.2). They are more receptive to issues of social justice, political transparency, and economic prospects. Their protest sentiments often arise from dissatisfaction with career opportunities, levels of social mobility, and the absence of genuine mechanisms for influencing political decisions. In addition, young people actively use digital communication, enabling quick mobilization and efficient coordination of protest activities. Growing protest sentiments among youth can signal a crisis in social integration and emphasize the need to revise existing mechanisms for incorporating young people into economic and political processes (Pickard, 2019; Sawyer et al., 2022; Ustyuzhanin et al., 2023).

In Kazakhstan—and in comparable semi-authoritarian settings—standard public-opinion surveys have repeatedly undercounted latent protest potential among youth. On the eve of January 2022, survey-based indicators failed to register the buildup of discontent. We posit three families of mechanisms behind this undercount: (i) preference falsification and social-desirability pressures; (ii) nonresponse and undercoverage of high-risk cells/regions; and (iii) closed/ephemeral digital coordination that eludes traditional survey frames. The problem this article addresses is how to monitor such latent dynamics with replicable, low-risk tools using secondary sources only.

1.1 Major research questions

RQ1. Why did standard survey indicators fail to register latent youth discontent ahead of January 2022 in Kazakhstan?

RQ2. Can a three-proxy monitoring set—acceptability of unauthorized protest, institutional trust, and digital embeddedness—improve early-warning diagnostics under semi-authoritarian constraints, and under what shock–trust–network configuration does activation risk peak?

This article addresses a single question: why survey-based indicators missed the buildup to January 2022 and how a three-proxy monitoring set (acceptability of unauthorized protest, institutional trust, digital embeddedness) can better detect latent mobilization among youth. We treat January 2022 as a critical-juncture hypothesis and adopt two guiding expectations: (E1) in high-barrier settings, the readiness–participation gap widens (acceptability rises faster than turnout); (E2) activation spikes when a region-specific ‘shock–trust–network’ configuration is present.

We do not field a questionnaire, do not design a new sample, and do not collect primary data. The study is a secondary-evidence monitoring exercise suited to semi-authoritarian contexts. Accordingly, we target diagnostic clarity (transparent proxies, explicit coverage note, and risk-of-bias discussion) rather than estimators requiring bespoke sampling. Claims are descriptive under 2019–2024 scope conditions.

Our claims are descriptive and bounded to Kazakhstan’s 2019–2024 context. We aim to:

G1 Diagnose and synthesize the undercount mechanisms that limit survey-based forecasting in this context;

G2 Operationalize latent protest potential via the three proxies using published sources only;

G3 Demonstrate an early-warning template on the 2019–2024 trajectory and identify the Kazakhstan-specific shock–trust–network activation configuration;

G4 Specify deployable measurement upgrades for future waves (indirect questioning, anonymous messenger-based panels, region-stratified boosts, short diary panels).

We retain two expectations: (E1) in high-barrier settings, the readiness–participation gap widens; (E2) activation risk peaks when a region-specific shock–trust–network configuration is present.

Section 3 details data and methods; Section 4 reports results; Section 5 discusses mechanisms and limitations; Section 6 concludes with policy and research implications.

The participation of Kazakhstani youth in protest actions requires separate analysis. It should be conducted on multiple levels, founded on the collection and thorough examination of reliable data. The current analysis represents a preliminary research step in identifying the reasons for youth involvement in the crisis that erupted in Kazakhstan in January 2022. Sociological studies carried out in the preceding period did not capture the latent accumulation of factors that led to destructive youth activity. None of those studies explicitly pointed to the emerging risk.

In recent years, Kazakhstan has witnessed a growing public interest in political and socioeconomic issues, especially among young people (Burkhanov et al., 2019; Kilybayeva and Ibadildin, 2023). This trend is reflected in active youth engagement in public life, criticism of governmental actions, participation in activist initiatives, and involvement in protest events. Youth protest sentiments have become a significant indicator of the level of social tension and public discontent with particular aspects of political and economic life.

The historical context of protest movements in Kazakhstan is critical to understanding the current situation. After the collapse of the Soviet Union in 1991, the country underwent a complex period of political and economic reforms, marked by a dominant state role and limited political competition. The younger generation, having grown up during this transition, encountered a dual reality: on the one hand, economic growth and integration into the global economy, and on the other, restricted political competition, curtailed freedom of speech, and limited civic activity. These factors shaped a specific perception among youth of opportunities for political and social participation, ultimately influencing the nature of protest movements.

Events in Zhanaozen in 2011 played a special role in forming protest sentiments; the oil workers’ protest was forcibly suppressed (Beisembayeva et al., 2023). This incident became a major milestone in the collective social memory and revealed the limits of permissible civic engagement in Kazakhstan. In subsequent years, protests tended to be localized, yet young people exhibited an increasing willingness to voice dissatisfaction with socioeconomic issues.

Protest sentiments reached a peak in January 2022, when Kazakhstan experienced one of the most extensive social crises in decades. The “January crisis” began with economic grievances triggered by a sharp rise in liquefied gas prices but soon evolved into large-scale protests featuring political demands. Youth played a pivotal role in these protests, making extensive use of digital platforms for coordination and mobilization. These events underscored both the elevated level of social tension and the presence of deep structural problems related to young people’s limited capacity to influence political and economic processes (Sheryazdanova et al., 2024; Mukhamediyev et al., 2023). Despite harsh repression, the January 2022 crisis coincided with a short-term spike in youth politicization (≈60% allowed for demonstrations in 2022; Youth, 2022); 33.9% found personal participation acceptable in 2023 (Youth, 2023), followed by a decline to 16.2% in 2024 (Youth, 2024); we therefore treat January 2022 as a critical juncture rather than a demonstrated turning point. Because survey indicators lag and triggers are exogenous, the timing and magnitude of mass contention are only weakly predictable; see Section 5.3 for details.

We define latent protest potential as the probability to participate if barriers are lowered to a socially acceptable threshold; we proxy it by (i) acceptability of unauthorized protest, (ii) trust in institutions, and (iii) embeddedness in digital networks.

Existing work on youth protest in authoritarian settings is either normative-qualitative or cross-national and macro-structural; it rarely (i) operationalizes latent protest potential with replicable measures, (ii) links forecasting errors to identifiable survey mechanisms (preference falsification, nonresponse, closed digital coordination), or (iii) specifies a country-level activation configuration. We contribute on all three fronts by (1) defining a three-proxy monitoring set (acceptability of unauthorized protest; institutional trust; digital embeddedness), (2) consolidating forecasting-limit mechanisms in Section 5.3, and (3) isolating a Kazakhstan-specific shock–trust–network triad tied to January 2022 and the 2019–2024 trajectory.

Our framework aligns with findings from other authoritarian and hybrid regimes: youth-led contention is most likely when material shocks coincide with legitimacy dips and low-cost, networked coordination (Bayat, 2013; Tufekci, 2017; Beissinger, 2022; Robertson, 2011; Almeida, 2019; Chenoweth and Stephan, 2011).

We analyze a single-country case (Kazakhstan) in 2019–2024. Our claims concern threshold mechanisms under a resource-dependent, semi-authoritarian regime with episodic repression and high Telegram/WhatsApp penetration. We do not assert universality; mechanisms and effect sizes are expected to vary where party competition is open, independent unions are strong, or digital channels are tightly censored.

2 Theoretical framework and literature review

To situate our Kazakhstan case in established scholarship, this section reviews four literatures—threshold and information mechanisms; repression and opportunity; digital coordination; and survey measurement under constraint—and then states our author interventions as portable propositions that anchor the empirical design that follows.

Classic threshold models explain how small changes in perceived social support can trigger large shifts in collective action (Granovetter, 1978). Preference falsification (Kuran, 1995) and informational cascades (Lohmann, 1994) imply that protest propensities are partly hidden until shocks alter expectations. In hybrid regimes, such dynamics interact with evolving political opportunity structures (McAdam et al., 2001), making monitoring of preconditions more realistic than event prediction.

Repression shapes the readiness–participation gap by raising expected costs of turnout (Davenport, 2007). Youth cohorts, concentrated in cities and exposed to global norms, are often pivotal in nonviolent mobilization (Chenoweth and Stephan, 2011; Beissinger, 2022). Following Tarrow (2011), we treat opportunities and threats as co-evolving with grievances; under semi-authoritarian constraints, conversion from attitudinal openness to street turnout remains structurally dampened.

Networked media lower coordination costs and enable rapid diffusion, yet authorities selectively target signals that facilitate collective expression (Tufekci, 2017; King et al., 2013). Cross-national evidence links social media penetration to protest participation in restrictive contexts (Enikolopov et al., 2020; Steinert-Threlkeld, 2017). Internet shutdowns and targeted censorship thus operate directly on activation thresholds, consistent with our focus on digital embeddedness as a proxy.

Measurement under constraint: sensitive attitudes and nonresponse. In settings with sanction risk, direct questions on contentious acts suffer from social desirability and item sensitivity (Tourangeau and Yan, 2007; Krumpal, 2013). Indirect elicitation (list/endorsement experiments) improves prevalence estimates of stigmatized behaviors (Blair and Imai, 2012). Separately, nonresponse can systematically exclude distrustful or high-risk subpopulations; meta-analyses document that lower response rates often correlate with measurable composition biases (Groves and Peytcheva, 2008). These diagnostics motivate our reliance on secondary indicators and the proposal of feasible upgrades (indirect questions, messenger-based panels, regional boosts).

Author interventions: a monitoring logic for semi-authoritarian contexts. We formalize our contribution as three portable propositions that integrate the literatures above with the Kazakhstan case and our three-proxy monitoring set:

P1 (Activation configuration). The probability that latent protest potential converts into turnout rises when a shock–trust–network configuration is present: (i) locally salient price/income shocks; (ii) a legitimacy downswing; (iii) high digital embeddedness lowering coordination costs.

P2 (Conversion constraint). Under semi-authoritarian repression, the readiness–participation gap widens with expected sanction severity; attitudinal openness is thus a leading indicator rather than a direct predictor of turnout.

P3 (Measurement advantage). In high-sensitivity environments, indirect questioning and anonymity-first sampling outperform direct questions for estimating contentious attitudes; secondary indicators can be used to monitor preconditions when primary measurement is infeasible.

Table 1 summarizes how the four literatures map onto our three-proxy monitoring set; this motivates the descriptive empirical design in Sections 3–4. Note. Proxies are measured from published indicators only; digital materials are used for narrative triangulation and make no representativeness claims.

Table 1

Table 1. Monitoring template — Mechanism → Proxy → Expected movement → Typical sources.

3 Data and methods

This article relies exclusively on secondary sources. We compiled indicators from (i) the Youth of Kazakhstan annual surveys (2019–2024), (ii) ACLED protest-event counts (2018–2024), (iii) Gallup’s (2022) trust measures, and (iv) KISI’s (2022) post-January trust survey, alongside policy and media chronologies listed in the References. We collected no new survey data and conducted no fieldwork; all figures cited are taken from published sources. We study a single-country case selected for theoretical relevance (semi-authoritarian, resource-dependent, high messaging-app penetration). Findings speak to threshold mechanisms under these conditions and are not claimed to generalize to competitive democracies or heavily censored autocracies.

Source-selection protocol (inline). Inclusion criteria: (i) recurrent and publicly documented series with stable wording/metadata; (ii) national or country-representative coverage; (iii) field dates that anchor the 2019–2024 window (or January 2022). Exclusion criteria: undocumented one-off polls; non-public microdata; restricted or scraped content. For each source we follow published definitions and do not infer unreported breakdowns.

The methodology of this study rests on an integrated combination of theoretical and empirical approaches, facilitating a multidimensional analysis of youth protest activity in Kazakhstan. Institutional analysis served as the theoretical foundation by identifying structural causes underlying the crisis in political participation, while discursive and comparative analyses clarified the characteristics of protest movements in both the post-Soviet context and broader global trends. Adopting an interdisciplinary perspective—incorporating political science, sociology, and economics—not only documents empirical patterns but also positions them within established theoretical paradigms.

To investigate the dynamics of youth protest activity, a secondary data analysis was conducted, encompassing a systematic review of scholarly publications, archival sociological materials, international organization reports, and media sources. This strategy enabled the construction of a sourced narrative summary of protest sentiment, the identification of key factors influencing its evolution, and an assessment of how political and socio-economic conditions shape these processes. A pivotal component of the empirical investigation involved examining quantitative data on protest activities in Kazakhstan from 2018 to 2024 (ACLED) and pre-2019 patterns from published chronologies; from these sources we extract aggregate frequencies, geographic distributions, and reported turnout where available. We do not compile a proprietary dataset or estimate youth shares from primary records. We employ descriptive statistics only (frequencies, proportions, year-over-year trends) to summarize published indicators; no clustering, correlations, or model estimates are reported in this article. Analytic aim is diagnostic rather than predictive; we use the three-proxy set to assess expectations E1–E2 under the stated scope conditions.

Following the study’s focus on latent mobilization, we use three proxies available in published sources: (a) acceptability of unauthorized protest (attitudinal readiness), (b) trust in political institutions (e.g., national government), and (c) digital embeddedness (self-reported use of networked platforms). We additionally track price-pressure indicators to interpret the 2022 spike. These measures correspond to the barriers and opportunity structures theorized in Sections 5.1 and 5.3.

Feasible instruments (not implemented here). To address the documented biases, future waves in this context should deploy: (i) indirect questioning—list/unmatched-count or endorsement experiments—to reduce preference falsification; (ii) anonymity-first sampling via online panels and messenger-app links with randomized invite trees to mitigate interviewer effects and nonresponse; (iii) region-stratified boosts (west/mono-industrial towns; renters/unemployed) to cover high-risk cells; (iv) short rotating diary panels (4–6 weeks) to capture threshold dynamics; and (v) qualitative digital ethnography of closed Telegram/WhatsApp groups under a minimal-risk protocol. We describe each instrument and its bias target in Section 5.3.

To achieve a more nuanced exploration of contextual and developmental aspects of protests, a case study method was employed, which enabled process-tracing of sequences and plausible mechanisms in specific episodes; no causal identification is claimed. The study encompassed the most significant youth-led protest events of the past 15 years, encompassing environmental demonstrations, student protests, and large-scale political rallies. This reconstruction-based approach made it possible to examine preconditions, the course of events, and respondents in these surveys’ demands, as well as to evaluate the responses of both governmental authorities and society. Within the case analysis, we used a sourced narrative to summarize protest events, drawing on verified accounts, news reports, and open social media materials.

Additionally, digital data and youth activity on social networks were examined to uncover mechanisms of online mobilization within protest movements. Technical note (digital corpus and processing). We bounded the corpus to December 2021–February 2022 and reviewed public Telegram channel archives and national media reports for three episode clusters (western regions, Almaty, and a regional-city cluster). Posts and reports were hand-coded by the authors using a short codebook (trigger type, barrier type, coordination cue); we archived URLs/timestamps and retained only public materials. No scraping or login-only content was accessed; no automated topic models, embeddings, or social-graph metrics were estimated. This qualitative scan serves triangulation only and does not claim representativeness. This qualitative scan is contextual and non-representative; it is used for narrative triangulation only and does not constitute primary data collection.

To contextualize survey patterns, we qualitatively scanned public digital sources (policy chronicles and open social channels) and used them for narrative triangulation only; we do not present automated discourse models or social-graph outputs in this article. We bounded the qualitative corpus to December 2021–February 2022 and drew on public Telegram channel archives, national media reports, and peer-reviewed studies (e.g., Kudaibergenova, 2022; Ternov et al., 2024; Troitskiy et al., 2024). We purposively sampled high-salience posts and reports from at least three key episodes (western regions, Almaty, and a regional city cluster) and coded triggers, barriers, and coordination cues. No identities were recorded; all materials were public; no automated topic models or social-graph outputs are reported in this article.

We analyzed publicly accessible content from Telegram channels, Instagram pages, and YouTube videos during December 2021–February 2022, with a broader contextual scan for 2019–2024. We sampled high-follower public channels and verified media/NGO pages that posted regularly on civic topics, and retrieved items containing Kazakh/Russian keywords relevant to the January events (e.g., “Қаңтар,” “Жаңаөзен,” “газ,” “митинг/протест”). Items were deduplicated and screened for relevance to youth participation, barriers, or coordination. We then applied a two-stage manual coding: (i) descriptive topical tags (trigger, barrier, coordination, repression, frame) aligned with the typology in Section 5.1; (ii) episode-level memoing to support process-tracing in the case narrative. We did not compute network metrics or run automated topic models; digital evidence is used for narrative triangulation only.

By employing a multifaceted methodological framework that integrates quantitative, qualitative, and digital analyses, this study offers a multilayered perspective on youth protest activity in Kazakhstan. It identifies the patterns underlying protest development and uncovers the institutional, economic, and social determinants involved. Such a design ensures high reliability of the findings and supports the formulation of forecasts regarding the future trajectory of protest movements in the country.

Ethics and availability. All data are aggregated and publicly available; no human subjects were contacted. Digital materials were limited to public posts/pages; no private groups were accessed and no restricted content was scraped. We did not collect or store personal identifiers; when referencing posts, we paraphrase and omit handles unless content comes from organizational accounts. Full source citations are provided in the References; links to illustrative public items are available from the authors upon request.

4 Results

Unless otherwise noted, all figures synthesize secondary sources (ACLED; the Youth of Kazakhstan 2019–2024 series; Gallup; KISI); no original fieldwork was conducted. References to social media illustrate narratives only and are not based on automated modeling or private-group data.

In recent decades, the protest activity of Kazakhstani youth has undergone a discernible evolution. In the 1990s and 2000s, young people’s participation in political protests was sporadic and relatively low. Sociological data confirm that the proportion of politically engaged youth remained small over an extended period, with only about 15% of young individuals expressing an interest in political processes (Burkhanov et al., 2019). Traditionally, the authorities strictly suppressed independent civic activism among young people, which led to apathy and caution among the younger generation.

However, by the late 2010s, the situation began to change: 2019 became a turning point, when the first presidential election held without Nursultan Nazarbayev’s participation was accompanied by a surge in youth protests and the emergence of new informal movements. Subsequently, young people increasingly took part in socio-political actions, culminating in their significant role in the mass protests of early 2022.

The rise in protest activity among Kazakhstani youth is not solely a response to isolated events but rather reflects deeper socio-political transformations facilitated by the accelerated political socialization of this new generation. Published analyses indicate that the expansion of the information environment plays a pivotal role in this process, fostering a more critical perception of reality and a heightened awareness of civil rights (Sawyer et al., 2022; Ustyuzhanin et al., 2023). Unlike older generations socialized under conditions of limited information access, millennials and Generation Z came of age amid digital openness, where the internet and social media serve not only as primary communication channels but also as platforms for civic mobilization. As of 2023–2024, independent digital statistics report that roughly two-thirds of Kazakhstan’s population uses social media; uptake is higher among urban youth (StatCounter Global Stats, 2025). Global content obtained through digital channels introduces young people to democratic values, while opportunities to study abroad, participate in international exchange programs, and benefit from online education foster comparative analyses of various socio-political systems (Pickard, 2019; Sawyer et al., 2022).

Nevertheless, information openness is only one factor driving the rise in youth political consciousness. Domestic socio-economic challenges, including the rising cost of living, a labor market crisis for recent graduates, diminished access to housing, and the expense of higher education, create acute material incentives for civic engagement. Published survey data indicate that price pressures, job insecurity, housing costs, and credit obligations are the most frequently cited drivers of protest readiness; in 2023, 37.1% rated rising prices a “high-probability” trigger and 25.3% a “moderate” one (Youth, 2023). Young people recognize that these problems are not purely market-driven or external; instead, they have structural causes related to governance and resource allocation, which renders political activism not merely a possibility but also a necessary means of expressing civic positions.

Protest activity among Kazakhstani youth appears at the intersection of two interrelated processes: the expansion of their informational horizon, which promotes an understanding of democratic norms and rights, and the buildup of socio-economic grievances, which generates a persistent demand for change. This synergistic effect is associated with a gradual development of youth protest potential, manifested not only in spontaneous demonstrations but also in more institutionalized forms of political participation.

Published youth surveys consistently report limited institutional channels for influence; consequently, public demonstrations are perceived as a way to draw attention to youth issues (Youth, 2019, 2020; FES, 2021a,b). In these surveys, respondents note that only public displays of dissent compel authorities to pay attention to youth issues and broader societal problems. Some skepticism remains—youth initially loyal or inexperienced doubt efficacy and fear sanctions—yet protest is gradually normalized as an acceptable form of civic engagement.

Youth turnout is suppressed by three barrier families—legal repression, social stigma, and economic vulnerability—producing channel substitution into online activism, volunteering, or exit; the full typology and mechanisms are in Section 5.1. To ground these mechanisms, we include three brief sourced vignettes from January 2022 that illustrate how triggers, barriers, and digital coordination interacted in practice.

From 2017 to 2019, a consistent downward trend in the likelihood of mass protest actions was observed (Youth, 2019). In 2020, however, there was an increase in the proportion of respondents who considered protest activity possible, which can be explained by an increasingly complex socioeconomic context arising from both external shocks and internal structural changes (Youth, 2020). Although in 2021 the percentage of young people who regarded protests as an acceptable means of expressing dissatisfaction ranged from 15 to 18.6% (Youth, 2021), findings from 2022—collected after the high-profile events of January—revealed that approximately 60% (Youth, 2022) of respondents partially allowed for the possibility of demonstrations. Moreover, 18.7 and 22.6% (Youth, 2022), respectively, identified participation in unauthorized activities as an acceptable and an effective method of civic engagement.

Core series recap: personal acceptability of participation—2019: 18.7%; 2021: 15–18.6%; 2022: ≈60% allowed demonstrations; 2023: 33.9%; 2024: 16.2% (Youth, 2019, 2020, 2021, 2022, 2023, 2024).

Where the Youth series did not publish gender/region cross-tabs for a given year/indicator, we do not infer or estimate them; subgroup claims are therefore limited to what sources explicitly report.

In 2023, public attention shifted to escalating socioeconomic problems, primarily related to rising prices for food, medication, and utilities, which was accompanied by an increase in the number of respondents who deemed personal participation in protests permissible. In 2024, however, this willingness to participate declined. An analysis of determining factors points to the dominance of socioeconomic causes—such as price increases, payment delays, unemployment, housing issues, and credit obligations—which exert the most significant influence on the formation of protest attitudes among young people. Political factors (including corruption, legal violations, and ineffective governance) are comparatively less influential; nevertheless, they retain a certain impact, particularly during periods of heightened social conflict. The 2023 attitudinal peak is consistent with the salience of price pressures reported in Youth (2023) (37.1% ‘high’, 25.3% ‘moderate’); this pattern is consistent with a price-shock–related spike rather than a secular shift; causal identification is beyond the scope of this article.

In Kazakhstan, higher mobilization risk appears when a region-specific shock–trust–network configuration is present: (i) a localized price shock (e.g., LPG liberalization in western regions in January 2022), (ii) a trust downswing to ~54% by end-2021 (Gallup, 2022), and (iii) high digital embeddedness lowering coordination costs among urban youth (Youth, 2022, 2023, 2024); we treat this as a monitoring hypothesis (E1–E2), not a causal estimate. The 2019–2024 trajectory is thus consistent with economic-salience–led activation (33.9% personal acceptability in 2023, falling to 16.2% in 2024) rather than a secular shift in contentious norms (Youth, 2022, 2023, 2024). This activation logic is context-specific and should not be extrapolated to settings with open party competition, strong labor unions, or stringent digital censorship.

This configuration adds specificity to prior Kazakhstan accounts by combining known ingredients into a threshold logic and an operational monitoring template suitable for repeated use. Rather than attributing mobilization to generic “crises,” it specifies how price shocks, trust downswings, and networked coordination interact to lower activation thresholds. A comparable shock–legitimacy–network logic is reported in cross-regional studies of youth contention in hybrid regimes, which strengthens external validity of our Kazakhstani case (Bayat, 2013; Beissinger, 2022; Almeida, 2019). This configuration is country-specific; outside Kazakhstan, its components and thresholds should be re-estimated before use.

We therefore use surveys to map preconditions rather than events; mechanisms behind forecast error are discussed in Section 5.3. This mapping is descriptive and bounded to Kazakhstan’s institutional and digital environment; we do not claim out-of-sample event prediction. For future monitoring, Section 5.3 specifies context-appropriate instruments—indirect questioning, anonymous messenger-based sampling, region-stratified boosters, and short diary panels. It is also significant that young respondents themselves may be unaware of their readiness to protest until the moment of mobilization, as their positions and sentiments form in real time under the influence of social media and personal online communities—arenas of self-organization that remain insufficiently explored by traditional sociology.

5 Discussion

Drawing on published indicators rather than new fieldwork, we interpret the 2019–2024 trajectory through threshold, repression–opportunity, and digital-coordination lenses. The Youth of Kazakhstan series shows attitudinal openness peaking around January 2022 (~60% allowed demonstrations), easing to 33.9% in 2023 and 16.2% in 2024 (Youth, 2019, 2020, 2021, 2022, 2023, 2024); ACLED records 2,591 protest events in 2019–early 2022, ~77.5% peaceful (Armed Conflict Location and Event Data Project (ACLED), 2025–early 2022). Trust series comove with these shifts: Gallup reports a decline in confidence in the national government to ~54% by end-2021, while KISI registered a post-crisis rebound in presidential trust to 81.4% in February 2022 (Gallup, 2022; KISI, 2022). In the youth domain, FES (2021a,b) finds only 2.6% with protest experience, 78.7% never participated, yet 15.3% do not rule out joining—consistent with a persistent readiness–participation gap (FES, 2021a,b). Interpreted via threshold models (Granovetter, 1978; Lohmann, 1994) and preference-falsification under sanction risk (Kuran, 1995; Tourangeau and Yan, 2007; Krumpal, 2013), these secondary series suggest that activation rises when a shock–trust–network configuration is present and conversion to turnout remains structurally dampened by legal, social, and economic barriers (Davenport, 2007; Chenoweth and Stephan, 2011; Tarrow, 2011; Bayat, 2013; Beissinger, 2022). Digital embeddedness—documented in Youth reports and complementary platform statistics—lowers coordination thresholds, while selective censorship and shutdowns reshape signals (Tufekci, 2017; King et al., 2013; Enikolopov et al., 2020). Our discussion therefore uses secondary written sources as evidence to interpret mechanism-level patterns rather than to estimate new effects.

First, the trust downswing to ~54% by end-2021 (Gallup, 2022) aligns temporally with the price-shock onset and the Youth-series spike in acceptability (~60% in 2022), followed by a normalization in 2023–2024 (33.9% → 16.2%). Read through information-cascade logics (Lohmann, 1994) and conversion-cost arguments (Davenport 2007), this comovement is consistent with E1–E2: readiness shifts faster than street turnout when sanctions and stigma remain high.

Second, ACLED’s 2,591 events (2019–early 2022, 77.5% peaceful) provide the contextual ceiling for potential street action; the narrow youth protest experience (2.6%) in FES (2021a,b) demonstrates why conversion stays low despite attitudinal openness. Together with KISI’s February-2022 rebound in presidential trust (81.4%), these secondary series support a punctuated activation rather than a secular liberalization of contentious norms.

Causal scope and counterfactuals: we do not claim that price shocks or digital coordination caused mobilization. Alternative explanations—regional policing cycles and repression intensity, media-agenda spikes, sampling drift across annual Youth surveys, or changes in ACLED coverage—may contribute. Future work should probe the shock–trust–network mechanism with design-based strategies or short panels and test P3’s measurement advantage via indirect-questioning modules and anonymity-first sampling.

The Kazakhstan pattern—urban-youth networked contention under high sanction risk—matches cross-national evidence from hybrid regimes, where material shocks plus legitimacy dips plus low-cost coordination raise activation risks (Bayat, 2013; Chenoweth and Stephan, 2011; Almeida, 2019; Beissinger, 2022; Enikolopov et al., 2020). Consistent with King et al. (2013), interventions that suppress collective expression rather than criticism per se modulate thresholds without eliminating mobilization. We therefore treat the shock–trust–network triad as a monitoring diagnostic, not as a causal claim.

5.1 Socioeconomic and political-legal determinants of protest sentiments among youth in Kazakhstan

Youth-reported price pressures (e.g., 37.1% “high probability” trigger in Youth, 2023, plus 25.3% “moderate”) and persistent legal barriers [assembly restrictions and penalties summarized in Youth (2021) and FES (2021a)] jointly elevate expected costs, explaining why personal acceptability (33.9% in 2023) exceeds local turnout (conversion dampening). In line with Davenport (2007) and Tarrow (2011), repression risk produces channel substitution (online activism, volunteering, exit). The January-2022 shock in western regions—paired with the trust downswing (Gallup, 2022)—fits a punctuated-equilibrium interpretation of thresholds (Granovetter, 1978; Lohmann, 1994), after which normalization (Youth, 2024: 16.2%) resumes.

Beyond material concerns, value-oriented claims (justice, speech, access) are present, but barriers keep turnout low; this pattern aligns with prior studies. Prior surveys report that the main barriers to more extensive youth protests remain repressive legislation and the use of force (Youth, 2021; FES, 2021a). These barriers increase the expected personal cost of street action relative to safer alternatives (online activism, volunteering, exit), depressing conversion from willingness to turnout. Hence, the readiness–participation gap persists even during peaks of discontent. In Kazakhstan, the conversion from willingness to turnout appears to have risen when an LPG price shock in the west coincided with a trust downswing and networked coordination; we cannot rule out confounding from repression cycles, media coverage, or measurement artifacts. For a long time, authorities nurtured a subculture of passivity in which “not standing out” became a survival strategy. Nonetheless, the current generation is gradually discarding this stereotype. Future scenarios for the development of youth protest activity depend largely on the state’s actions. If the promised political reforms—for instance, genuine election liberalization and engagement in societal dialogue—are implemented, some of the protest energy may transition into constructive channels, such as youth participation in lawful politics, the creation of new parties, or the establishment of NGOs. If, however, reforms prove superficial and socioeconomic pressure (e.g., unemployment, inflation) intensifies, protests may reemerge and become still more organized and coordinated through digital networks. Youth in Kazakhstan have already gained experience in both successful mobilizations (2019) and in grappling with the serious repercussions of uncoordinated uprisings (2022). This experience could lead to more deliberate protests in the future, featuring carefully planned campaigns with explicit demands instead of spontaneous unrest. New youth leaders may emerge, or new forms of networked leadership could arise through popular bloggers and public opinion leaders. Similar conversion gaps under repression are documented across comparable contexts—post-Soviet and MENA cases as well as Latin America—where legal risks, stigma, and economic precarity divert youth from street turnout toward safer channels (Robertson, 2011; Bayat, 2013; Almeida, 2019; Beissinger, 2022).

In 2019–2024, youth contention in Kazakhstan was characterized by irregular, network-based self-organization and values-driven motivations; these patterns are contingent on the country’s semi-authoritarian setting and may differ in other regimes. Young people protest less for concrete material gains and more against systemic problems—namely injustice, lack of freedom, and limited rights (Junisbai and Junisbai, 2020; Kudaibergenova and Laruelle, 2022). The principal obstacles are repression and insufficient institutional channels of influence, whereas the driving forces are perceptions of marginalization (relative deprivation) and expanding communication opportunities. Protest culture has evolved from the near-complete lull of the 2000s to a more dynamic and assertive stance in the 2020s. Having experienced both expansions and tragedies, young people in Kazakhstan are developing a generational understanding of protest as an extreme yet sometimes necessary measure to compel authorities to heed the citizenry. Their future activism will be determined by the extent to which authorities themselves are willing to change and consider the interests of the younger generation. The past 30 years of history illustrate that, in the absence of dialogue, youth seek other avenues, and this raises the probability of protest cycles in the absence of credible dialogue mechanisms.

The historical trajectory of youth protests in Kazakhstan points to a growing gulf between the expectations of the new generation and prevailing political and social mechanisms. Recent decades show that without genuine dialogue and legally sanctioned channels of influence, protest does not arise spontaneously but often becomes a patterned response to systemic imbalances, conditional on contextual triggers and barriers. In this light, youth protests should be understood not as a transient phenomenon but as a structural process signaling the need to reform Kazakhstan’s system of political participation.

Associations reported here are descriptive; causal identification is beyond the scope of this article.

Youth embeddedness in Telegram/WhatsApp ecosystems lowers organizational thresholds (Tufekci, 2017; Enikolopov et al., 2020). In hybrid settings, authorities often tolerate criticism but suppress collective expression (King et al., 2013); shutdowns around January-2022 are thus mechanism-consistent with our monitoring lens. We do not infer representativeness from public-channel scans; rather, we interpret survey comovements with digital coordination as reported in published studies (Kudaibergenova, 2022; Ternov et al., 2024; Troitskiy et al., 2024).

5.2 Empirical data on youth protests in Kazakhstan

Official statistics on protests in Kazakhstan are fragmentary, but independent research has documented a noticeable rise in youth civic activity in recent years [Armed Conflict Location and Event Data Project (ACLED), 2025–early 2022; Kudaibergenova, 2022; Ternov et al., 2024]. According to the ACLED (Armed Conflict Location and Event Data Project) database, 2,591 protest actions took place in Kazakhstan from 2019 through early 2022, approximately 77.5% of which were peaceful demonstrations [Armed Conflict Location and Event Data Project (ACLED), 2025–early 2022]. Relative to pre-2019 tallies in ACLED’s Kazakhstan coverage, this represents a marked increase [Armed Conflict Location and Event Data Project (ACLED), 2025–early 2022]. The year 2019, in particular, set a record: the transfer of power coincided with a surge in protests over a wide range of issues, from economic to political. Youth participated actively in these demonstrations, especially in urban areas. Protest activity continued in 2020–2021 (despite the pandemic), encompassing solidarity rallies for political prisoners, environmental pickets, and feminist marches—often involving young people. The statistical peak occurred during the January 2022 events, followed by a downturn. Nevertheless, data indicate that since 2019, youth protest activity has no longer been an isolated phenomenon, instead displaying a sustained upward trend. Young people have increasingly expressed dissatisfaction publicly, at least through local demonstrations. For instance, whereas in 2018 the number of youth-led protests could be counted on one hand, in 2019–2021 they took place almost monthly. The geographic scope also expanded: beyond Almaty and the capital, youth protests were noted in Shymkent, Oral (Uralsk), Aktobe, and elsewhere. Thus, in purely quantitative terms, recent years have revealed a substantial surge in protest activity among young Kazakhstani citizens [Armed Conflict Location and Event Data Project (ACLED), 2025–early 2022].

Sociological research confirms that Kazakhstani youth remained politically passive for an extended period, but the situation is changing. According to a large-scale study by the FES (2021a), only 2.6% of young Kazakhstani respondents reported having taken part in demonstrations, while 78.7% stated they had never participated in a protest. The overwhelming majority of young people thus lacked direct protest experience, reflecting years of suppression. However, it is noteworthy that 15.3% of surveyed youth did not rule out the possibility of joining demonstrations in the future. This indicates a “hidden potential,” as one in six young Kazakhstani citizens is theoretically willing to protest under certain conditions. The gap between actual participation and declared willingness suggests that fear and lack of opportunities, rather than the absence of discontent, are the primary barriers. According to the survey, 59.1% of young people believe politicians pay no attention to youth perspectives. This sense of alienation from decision-making processes fuels protest sentiments, even if, for now, they more frequently manifest online or in private conversations. Overall, sociologists note that Kazakhstani youth appear apolitical only superficially: their low involvement in official structures such as political parties and elections coincides with profound distrust of these institutions. For example, most young people do not belong to political parties (77.2%) and do not trust elections, yet many express a civic stance through volunteering, environmental initiatives, or ethical product boycotts.

Thus, a latent protest potential exists. Young people are dissatisfied with current circumstances—corruption, unemployment, and rising prices—and are seeking ways, at least indirect, to exert influence. A 2016 survey by the Ebert Foundation revealed that the majority of young Kazakhstani citizens showed little interest in politics and did not participate in civic initiatives (Ebert, 2016). However, the year 2019 signaled a shift: new activists emerged following the elections. Indirectly, this is corroborated by research conducted after the events of January, which recorded a short-term spike in youth politicization, although protest sentiments subsequently receded from the public sphere (Kudaibergenova and Laruelle, 2022; Sheryazdanova et al., 2024; Troitskiy et al., 2024). Declared acceptability of contentious action exceeds actual participation, consistent with high latent potential under strong barriers. Published accounts document on-the-ground narratives of price shock, coordination via Telegram, and fear of sanctions (Kudaibergenova, 2022; Ternov et al., 2024; Troitskiy et al., 2024).

Details on digital data sources, coding criteria, and ethics are provided in Section 3.

Cross-validation. We cross-checked vignette dates and locations against ACLED event logs for 2–6 January 2022.

Vignette 1—Western regions (Mangystau, Jan 2–4, 2022). Published accounts document a sequence from LPG-price grievances to broader political claims, with youth visible in roadside gatherings and vehicle caravans; fear of sanctions shaped evening dispersal and route choices (Kudaibergenova, 2022; Ibadildin and Primiano, 2024; Armed Conflict Location and Event Data Project (ACLED), 2025–early 2022).

Vignette 2—Almaty (Jan 4–6, 2022). Telegram channels circulated decentralized calls to assemble; frames emphasized dignity and anti-elite accountability (“Shal, ket!”) while participants adapted tactics to escalating risk (Ternov et al., 2024; Kudaibergenova, 2022; Troitskiy et al., 2024).

Vignette 3—Regional cities (e.g., Shymkent/Aktobe). Smaller marches fused economic triggers (prices, utilities) with corruption narratives; subsequent channel substitution into online advocacy and volunteering is consistent with high barriers to street action [Armed Conflict Location and Event Data Project (ACLED), 2025–early 2022; Youth, 2023; Sheryazdanova et al., 2024].

Nationwide surveys of young people show that in 2019, large-scale protests in local areas were deemed unlikely or impossible by 81.3% of respondents. In general, when compared to 2017 and 2018 data, 2019 saw a rise in the number of respondents who were fairly certain no protest actions would occur. In 2017, that figure stood at 70.6%, while in 2018 it was 72.5%. Meanwhile, the level of protest expectations in 2019 was 18.7%. Moreover, youth exhibited scant personal interest in participating in public protest actions, with 59.7% of respondents stating that “nothing could make them protest openly.” Slightly more than half noted a lack of reasons that might prompt them to join protests. Overall, this indicator has shown a positive trend from 2017 to 2019. Among the factors motivating youth protest participation, social and labor relations have become increasingly influential: wage or stipend delays, allowances, layoffs, or dismissals from work can serve as key drivers of youth protest (Youth, 2019).

An examination of data on the likelihood of protest actions between 2016 and 2019 suggests a favorable trend—a decline in the share of respondents indicating a risk of protests, from 32.1 to 18.7%. However, data from a June 2020 study point to heightened protest sentiments, with one out of four (25.1%) acknowledging the possibility of protest actions and 9.4% deeming it quite high. Concurrently, a total of 66.9% of surveyed young people ruled out the probability of protests in the region where they live (Youth, 2021).

In 2020, sociological research identified key protest-related factors, specifically the issues respondents found most troubling. Above all, socioeconomic concerns—rising prices for essential goods, job cuts and layoffs, problems in educational institutions and housing, negligence or unprofessionalism in healthcare, delays in various payments, unjustified fines, and increased credit or mortgage payments—could lead young people to participate in public protests. A smaller group of respondents cited political issues related to dissatisfaction with government actions, unfair judicial decisions, or discrimination based on ethnicity as motivating factors. Half of the respondents (50.9%) stated they would not take part in any protest actions under any circumstances (Youth, 2020).

In 2021, findings showed that 15 and 18.6% of respondents, respectively, regarded participation in unauthorized protests, rallies, pickets, or demonstrations as acceptable and effective ways of expressing dissatisfaction with conditions in the country or in their city or village. In 2022, immediately after the events of “Bloody January,” approximately 40% of young Kazakhstani citizens believed large-scale protests to be impossible, while 18.7 and 22.6%, respectively, deemed participation in unauthorized protests, rallies, pickets, or demonstrations acceptable and effective (Youth, 2022).

In 2023, respondents identified rising prices for food and medication as one of the most likely reasons for youth protests; 37.1% viewed this factor as highly probable, and 25.3% rated it as moderately probable. Corruption (28.3%) and higher utility rates (34.8%) were also widely seen as likely triggers. Personal participation in unauthorized protests, rallies, pickets, or demonstrations was acceptable to 33.9% of respondents (Youth, 2023).

Finally, in 2024, when asked, “Which forms of expressing dissatisfaction with the state of affairs in the country are personally acceptable or unacceptable to you?” 16.2% indicated that involvement in unauthorized protests, rallies, pickets, or demonstrations was acceptable, whereas 69.4% deemed it unacceptable (Youth, 2024).

5.3 Methodological limitations and hidden protest sentiments: Why did surveys fail to predict the events of January 2022?

We treat mechanisms such as preference falsification, nonresponse bias, and closed digital coordination as testable expectations rather than established causal estimates given the study’s descriptive design. The events of January 2022 came as a shock to both the authorities and the entire population of Kazakhstan. In retrospect, analysts and sociologists identified several reasons why public opinion surveys and forecasts proved inaccurate. This study examines the scientifically substantiated factors that hindered the prediction of the 2022 protests in Kazakhstan, ranging from methodological shortcomings to hidden shifts in public perceptions of political authority. Drawing on research and empirical examples, this analysis systematically identifies the reasons sociological forecasts were limited, including issues related to survey methodology, the hidden respondent effect, the influence of new media, and profound socio-economic and cultural factors.

In the context of Kazakhstan, nationwide surveys often failed to capture the most discontented segments of the population. Many surveys suffered from sample bias: certain groups—particularly rural youth, the unemployed, and marginalized populations—remained outside the sociologists’ field of observation. This methodological flaw resulted in inflated official indicators of public trust and satisfaction.

In late 2021, the Center for Strategic Initiatives conducted a large-scale survey on public trust in the government (CSI, 2021). The results were unexpected. One of the key findings was a relatively high trust rating for President Tokayev—68.5% of respondents expressed confidence in him. Over 2 years (2019–2021), this figure had increased by 7.3 percentage points (from 60.8% in 2019). However, independent studies indicated significantly lower trust in government institutions. According to Gallup (2022), confidence in the national government had declined to a record low of 54% by the end of 2021, down from 72% a year earlier. This discrepancy highlights flaws in the representativeness of official surveys and suggests that a substantial portion of the population’s latent discontent remained undetected by statistical analyses. After the events of “Bloody January,” surveys conducted in February 2022 reported that trust in the president had surged to 81.4% (KISI, 2022).

Traditional surveys recorded only explicit opinions, while the population’s latent discontent remained “beneath the surface.” In 2020, BISAM Central Asia, supported by the Friedrich Ebert Foundation, conducted a sociological survey on Kazakhstani youth (FES, 2021b). Many young respondents identified key issues, such as economic instability and corruption, as significant concerns but refrained from expressing overt opposition to the government. Analysts often misinterpreted the absence of radical responses as a sign of political stability. Moreover, Kazakhstan was frequently viewed as a homogeneous entity, with little consideration for regional disparities. However, conditions varied significantly across different regions: for instance, in the western part of the country (Mangystau Region), the population perceived corruption and social injustice with greater intensity.

The failure to account for regional disparities and localized tensions constituted a major methodological oversight by analysts. Consequently, the rise in protest sentiments in peripheral areas—particularly in western Kazakhstan, where the first demonstrations erupted on January 2, 2022—was not identified in time.

In the Kazakhstani context, respondents often concealed their true opinions due to fear of repercussions. This phenomenon, known as “preference falsification,” occurs when individuals publicly express loyalty to the government despite privately harboring dissatisfaction (Kuran, 1995; Wang, 2022). Concerns about potential political repression frequently led respondents to provide socially desirable answers, distorting the results of sociological surveys. For an extended period, Kazakhstan maintained an environment in which openly criticizing government institutions was perceived as unsafe. As a result, actual protest sentiments were rarely reflected in survey data: many of the most discontented citizens avoided participating in surveys altogether, fearing breaches of anonymity and unforeseen consequences.

This process of self-selection resulted in survey samples disproportionately composed of individuals who either demonstrated loyalty to the government or exhibited political apathy. Additionally, the “spiral of silence” effect emerged, whereby the apparent absence of critical voices in public discourse reinforced further silence. Consequently, society accumulated discontent in a latent form as individuals sought to avoid the risks associated with political sanctions.

However, as the case of Kazakhstan illustrates, conformity has its limits. By 2021, the proportion of citizens willing to “patiently wait for better times” had declined to 19%, reaching its lowest level in the entire period of observation (World Bank, 2022). The preceding analysis suggests that hidden discontent had been escalating, even though sociological surveys failed to capture it explicitly. Once fear subsided and an opportunity arose to express dissent—following the sharp increase in gas prices in January 2022—a sudden release of previously suppressed emotions occurred.

This study underscores the critical limitations of traditional survey methodologies in authoritarian and semi-authoritarian contexts. The inability to detect hidden protest sentiments highlights the need for more sophisticated research approaches, incorporating indirect indicators, qualitative assessments, and digital data analysis. These mechanisms mirror comparative evidence from authoritarian settings where preference falsification and digitally mediated coordination undermine survey-based forecasting (Kuran, 1995; Tufekci, 2017; Chenoweth and Stephan, 2011).

Practical alternatives for Kazakhstan (design notes). To mitigate the mechanisms diagnosed above, we outline deployable instruments suited to semi-authoritarian settings:

• Indirect questioning (List/Unmatched-Count; Endorsement). Insert a sensitive item (e.g., willingness to join an unauthorized rally) into aggregated lists or endorse issue bundles; estimate prevalence without individual attribution → targets preference falsification/social desirability; minimal extra cost per wave.

• Anonymous online panels with messenger-app recruitment. Seed randomized invite trees in Telegram/WhatsApp; avoid live interviewers; include one-time device attestation → targets interviewer effects and nonresponse bias among distrustful youth.

• Region-stratified oversamples. Boost cells in western regions/mono-industry towns and among renters/unemployed; weight back to population → targets geographic under-coverage visible in January 2022.

• Short rotating diary panels (4–6 weeks). Weekly check-ins on perceived risks, peer cues, and trigger salience; attrition-tolerant design → captures threshold dynamics missing in cross-sections.

• Qualitative digital ethnography of closed channels. IRB-style minimal-risk protocol; structured note-taking/codebook; temporal sampling around network shutdowns → targets closed/ephemeral coordination that eludes surveys.

Trade-offs: these designs improve measurement of preconditions and thresholds; they do not claim causal identification and must be paired with clear ethics safeguards.

Moving forward, integrating these methods into sociological research will be essential to improving the accuracy of political and social forecasts.

Contribution to forecasting research. We recast forecasting as measurement of preconditions rather than prediction of events, integrating three mechanisms—preference falsification, nonresponse bias, and closed/ephemeral digital coordination—into a practical diagnostic for semi-authoritarian contexts. This framing is situationally portable to analogous semi-authoritarian contexts with similar repression and digital-coordination profiles; external validity elsewhere remains to be tested.

The primary issue is that surveys failed to fully capture the composition and preferences of respondents. They overestimated public support for government actions and approval of state policies due to sampling bias, which disproportionately included more educated and urban respondents. Meanwhile, the driving force behind the protests consisted of citizens residing in rural areas, suburban settlements, and the outskirts of major cities. In other words, surveys inadequately represented precisely the demographic group that ultimately became the direct respondents in these surveys in the protests.

One of the widely cited explanations for the failure of forecasts is the phenomenon of “hidden protest sentiments” (Gulin and Dementieva, 2011). This concept suggests that a segment of protest-minded respondents refrained from expressing their views to sociologists—either due to reluctance to publicly associate themselves with opposition sentiments or distrust of surveys—thereby remaining unaccounted for in the data. However, no comprehensive studies have been conducted to substantiate this claim, and no direct evidence has been presented to demonstrate widespread dishonesty among respondents. There has been no systematic analysis proving that protest-oriented citizens deliberately concealed their intentions in surveys. However, this does not mean that the problem does not exist. Rather than respondents actively deceiving sociologists, the issue may lie in who actually agrees to participate in surveys in the first place. The so-called “nonresponse bias” has become a significant challenge in recent years. If certain groups of respondents systematically avoid answering survey calls and questionnaires, their opinions will be underrepresented. A substantial portion of the forecasting error, in our view, stems precisely from this systematic nonresponse—there are significant differences between those who participate in surveys and those who refuse. Specifically, we hypothesize that supporters of protest movements, influenced by media rhetoric portraying protests as “destructive,” were more likely to ignore surveys, effectively turning their refusal to participate into a form of political statement.

Indirect evidence supports the existence of this hidden segment. A World Bank (2022) identified approximately 10% of “likely protest-minded respondents” who could be categorized as “reluctant respondents”—individuals who tend to avoid survey participation. These individuals are generally less educated, more alienated from the state, and not motivated by a sense of “civic duty.” Since such respondents are systematically excluded from survey samples, an unaccounted-for layer of protest support emerges, which remains underestimated in forecasts.

Thus, the “hidden respondent effect” manifests not so much through deliberate deception of sociologists but rather through the selective nonparticipation of certain respondent segments in surveys. Traditional survey methods, such as phone calls and online panels, often fail to reach distrustful or “reserved” supporters of protests, leading to systematic distortions in results. Sociologists face the challenge that a significant portion of their respondent base avoids engagement altogether, and standard weighting adjustments (by gender, age, education, etc.) do not fully resolve the issue if surveys simply fail to capture a representative sample of protest-minded citizens.

Modern communication technologies played a pivotal role in the unexpected escalation of the protests. Social media platforms, messaging apps, and independent information channels enabled nationwide coordination of demonstrations within days, bypassing traditional political structures. The January protests were spontaneous and decentralized—they lacked clear leaders, unified slogans, or official organizers. Instead, coordination occurred horizontally, facilitated by a networked chain of calls to action and viral videos disseminated via Telegram channels, WhatsApp groups, and Facebook posts (Kudaibergenova, 2022; Ternov et al., 2024; Ibadildin and Primiano, 2024). This “networked” diffusion of protest activity caught experts off guard, as they were accustomed to tracking visible opposition parties or movements. In reality, protestors self-organized through extralegal channels of political communication—platforms beyond government control, such as exile opposition pages and activist chat groups. This complicates forecasting: traditional sociological methods are not equipped to monitor hidden information flows in closed social media groups.

Another crucial factor was the informational environment, in which the rapid spread of protest news in one city immediately inspired demonstrations in others. A “second-wave effect” emerged, where online videos of confrontations and government crackdowns triggered a chain reaction of outrage. Recognizing the power of digital coordination, the Kazakhstani authorities blocked the internet and social media at the height of the crisis in an attempt to disrupt protest organization. This intervention underscores the significant role of digital communication technologies in fueling the protests. Traditional analysts had initially underestimated the influence of social media, as previous protests (before the smartphone era) required longer preparation and public visibility. In today’s world, however, mass outrage can erupt almost instantly, bypassing official channels. Thus, new media enabled a level of spontaneity that traditional forecasting methods struggle to capture.

Fundamental socio-economic disparities in Kazakhstan created fertile ground for an uprising. The country was known for its high economic growth rates and substantial oil and gas revenues, yet the benefits of this development were distributed highly unequally. By the early 2020s, just 162 individuals controlled 55% of the country’s wealth—most of them relatives or associates of former President Nazarbayev (Commander and Prieskienyte, 2022). Household surveys report widespread low incomes in the bottom deciles; see World Bank (2022) for distributional detail. This extreme wealth gap fostered a deep sense of injustice. Ordinary citizens watched as the ruling elite amassed wealth and transferred capital abroad, while at the local level, job opportunities remained scarce, prices soared, and living standards declined. Inflation accelerated, with pronounced increases in food and fuel categories. The COVID-19 pandemic further exacerbated economic hardship for many families. This macroeconomic stress, combined with a sense of betrayed expectations (as national resources never translated into widespread prosperity), generated a reservoir of simmering discontent. Published analyses argue that the January 2022 unrest reflected deteriorating macro-conditions compounded by abrupt fuel-price liberalization in a fragile region (Mukhamediyev et al., 2023; Ibadildin and Primiano, 2024).

Beyond material factors, the cultural and political context that developed in Kazakhstan over several decades must also be considered. The country’s political system was characterized by low competitiveness, restrictions on freedom of expression, and the cultivation of a personality cult around Nazarbayev. This environment fostered a sense of diminished dignity and a lack of justice among the population. The younger generation, which had no memory of the early years of independence, was increasingly intolerant of the absence of genuine political rights and elite corruption. By 2019–2021, a culture of protest had begun to emerge in Kazakhstan: even in the absence of prominent opposition leaders, citizens increasingly participated in localized demonstrations for various causes. ACLED data show a steady rise in protest counts since 2019 [Armed Conflict Location and Event Data Project (ACLED), 2025–early 2022]. This suggests a value shift—for a growing segment of society, protesting was no longer taboo. The January events were, in many ways, an “uprising of dignity”—protesters demanded not only lower gas prices but also the resignation of the old political elite (“Shal, ket!”—“Old man, leave!”), anti-corruption measures, and the right to elect their government.

Thus, the cultural dimension—accumulated fatigue with paternalism, the desire for respect, and a broader role in societal decision-making—played a crucial role in the January events. However, such sentiments are not always easily quantifiable and often manifest in implicit forms, making them difficult to fully incorporate into predictive models.

External validity is bounded: the diagnostic is portable to semi-authoritarian settings with analogous media and control structures, but transfer requires context-specific calibration.

Given these limitations, we interpret co-movements as suggestive rather than causal and refrain from counterfactual claims beyond descriptive scope.

Taken together, the 2019–2024 evidence aligns with our three propositions. P1 (activation configuration) receives support: mobilization risk is highest when a Kazakhstan-specific shock–trust–network constellation is present (regional price shocks, a legitimacy downswing to ~54% by end-2021, and high digital embeddedness). P2 (conversion constraint) is visible in the persistent readiness–participation gap (e.g., personal acceptability reached 33.9% in 2023 while turnout remained dampened by legal, stigma, and economic barriers), and P3 (measurement advantage) is reflected in the relative performance of secondary indicators and motivates the proposed upgrades (indirect questioning, anonymous messenger-based panels, region-stratified boosts, short diary panels) for earlier signals under semi-authoritarian constraints.

Divergence between CSI, 2021 (elite-proximate, higher reported trust), Gallup (2022) (~54%), and KISI (2022) (post-crisis 81.4%) is interpretable via item sensitivity and composition differences (Tourangeau and Yan, 2007; Krumpal, 2013; Groves and Peytcheva, 2008). We therefore ground our interpretation in cross-source convergence rather than any single survey, using secondary series to reinforce the descriptive claims about thresholds, conversion costs, and networked coordination.

Under our secondary-source monitoring lens, activation risk in 2025–2026 should be read through three observable families that already appear in published indicators: (i) price/income shocks (policy chronologies), (ii) legitimacy swings (trust snapshots), and (iii) coordination affordances (platform penetration, censorship/shutdown episodes). Convergent movement in all three—similar to January 2022—would signal elevated risk; divergence suggests continued dampening of conversion. We refrain from prediction and treat these as diagnostics for ongoing descriptive monitoring.

6 Conclusion

We summarize measurement limits and improvements inline and recast forecasting as monitoring of preconditions with secondary indicators. Methodological limits and improvements are summarized in Section 5.3; here we offer policy implications and near-term research directions. While the empirical base is Kazakhstan-specific, the proposed monitoring set and activation logic are consistent with comparative research on youth contention in authoritarian and hybrid regimes (Bayat, 2013; Tufekci, 2017; Beissinger, 2022; Robertson, 2011; Almeida, 2019). Our inferences are bounded to Kazakhstan’s 2019–2024 context; policy transfer to other settings should follow a prior diagnostic of regime type, union strength, and digital-media constraints. Triangulating survey proxies with sourced narrative vignettes strengthens mechanism-level interpretation without introducing new fieldwork.

Near-term research directions. Extend the Youth of Kazakhstan series with indirect-questioning modules (list/endorsement experiments) on protest acceptability; launch a small rotating online panel with regional strata and re-contact to track conversion from willingness to turnout; ethically curate a Telegram/WhatsApp sample for qualitative coding to triangulate with survey trends; and pre-register operational definitions, code, and a replication bundle to improve transparency.

For Kazakhstan, escalation risk is highest when regulated-fuel price shocks in western regions intersect with trust downswings and low-cost digital coordination, a configuration more policy-actionable than generic “crises.” Latent potential persists despite repression cycles; monitoring should therefore combine surveys with digital trace and qualitative indicators.

Latent potential persists despite repression cycles; monitoring should therefore combine surveys with digital trace and qualitative indicators.

Policy Recommendations (end-of-study list, evidence-informed by 2019–2024 scope)

1. Phase and regionalize price reforms. Pre-announce energy/utility price changes; sequence reforms in trigger-prone western oblasts; pair with targeted, time-limited compensation for youth and low-income households.

2. Publish service-delivery and accountability metrics. Release quarterly, open datasets on youth employment, housing affordability, and service performance; commit to an independent review of January 2022 use-of-force and publish follow-up actions.

3. Lower the legal cost of lawful assembly. Replace prior authorization with notification for peaceful assemblies; cap fines and decriminalize first-time non-violent violations; streamline permit procedures nationwide.

4. Protect youth civic infrastructure. Legally guarantee independent student associations and campus civic clubs; establish student ombudspersons; prohibit punitive academic sanctions for peaceful civic engagement.

5. Institutionalize routine youth consultation. Convene quarterly councils with elected youth representatives across regions; integrate recommendations into cabinet agendas and publish minutes and response memos.

6. Engage—rather than disrupt—digital channels. Maintain official Telegram/WhatsApp channels for issue reporting and emergency communications; avoid blanket internet shutdowns and adopt narrowly tailored, time-bound measures with transparency reporting.

7. Stand-up an early-warning monitoring dashboard. Track, non-predictively, the three proxies used here—acceptability of unauthorized protest, institutional trust, and digital embeddedness—alongside price-pressure indicators; publish series as open data.

8. Targeted economic buffers for young households. Deploy short-term wage support/public works in mono-industrial towns and rental support for 18–29-year-olds during price spikes; evaluate by uptake and retention.

9. Regional equity investments. Prioritize transport, education, and skills programs in western regions; expand apprenticeships with private employers and track placement outcomes.

10. Communication discipline (“no-surprises” rule). Pre-announce reform calendars with plain-language Q&A; allow a two-week public comment window; publish myth-busting notes to reduce rumor cascades.

These actions target the shock–trust–network configuration identified in this study: phasing price shocks addresses triggers; transparency and consultation rebuild trust; legal/digital changes lower the coordination and sanction costs, narrowing the readiness–participation gap without suppressing lawful voice.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

ZK: Validation, Conceptualization, Methodology, Writing – review & editing, Writing – original draft. YB: Investigation, Writing – review & editing, Funding acquisition, Writing – original draft, Conceptualization, Project administration, Supervision, Methodology, Data curation. AB: Writing – review & editing, Writing – original draft, Visualization, Data curation, Formal analysis, Software.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. BR24993269 “Evolution and Transformation of Value Orientations in Kazakhstani Society During the Independence Period”).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

Almeida, P. (2019). Social movements: the structure of collective mobilization. Berkeley: University of California Press. doi: 10.2307/j.ctvd1c7d7

Crossref Full Text | Google Scholar

Armed Conflict Location and Event Data Project (ACLED) (2025). ACLED dataset: Kazakhstan (2018–2024). Available at: https://acleddata.com/platform/explorer (Accessed August 10, 2025).

Google Scholar

Bayat, A. (2013). Life as politics: how ordinary people change the Middle East. 2nd Edn. Stanford, California: Stanford University Press.

Google Scholar

Beisembayeva, D., Papoutsaki, E., and Kolesova, E. (2023). “The Zhanaozen uprising” in Mapping the media and communication landscape of Central Asia: an anthology of emerging and contemporary issues. London: Bloomsbury Publishing PLC, 309.

Google Scholar

Beissinger, M. R. (2022). The revolutionary city: Urbanization and the global transformation of rebellion. New Jersey: Princeton University Press.

Google Scholar

Blair, G., and Imai, K. (2012). Statistical analysis of list experiments. Polit. Anal. 20, 47–77. doi: 10.1093/pan/mpr048

Crossref Full Text | Google Scholar

Burkhanov, A., Bigozhin, U., Blum, D., Hanks, R. R., Insebayeva, S., Isaacs, R., et al. (2019). The Nazarbayev generation: youth in Kazakhstan. London: Bloomsbury Publishing PLC.

Google Scholar

Chenoweth, E., and Stephan, M. J. (2011). Why civil resistance works: the strategic logic of nonviolent conflict. New York: Columbia University Press.

Google Scholar

Commander, S., and Prieskienyte, R. (2022). The political economy of Kazakhstan: a case of good economics, bad politics? Russ. J. Econ. 8, 122–158. doi: 10.32609/j.ruje.8.90948

Crossref Full Text | Google Scholar

CSI (2021). Survey on the rating of trust in the authorities. Available online at: https://exclusive.kz/expertiza/politika/126862 (Accessed August 10, 2025).

Google Scholar

Davenport, C. (2007). State repression and political order. Annual Review of Political Science, 10, 1–23. doi: 10.1146/annurev.polisci.10.101405.143216

Crossref Full Text | Google Scholar

Ebert,. (2016). Youth in Central Asia: Kazakhstan. Based on a sociological survey. Research advisor: Prof. Klaus Hurrelmann (Berlin, Germany). Almaty, 2016. 260. Available online at: https://library.fes.de/pdf-files/bueros/kasachstan/13343.pdf (Accessed August 10, 2025).

Google Scholar

Enikolopov, R., Makarin, A., and Petrova, M. (2020). Social media and protest participation: evidence from Russia. Econometrica 88, 1479–1514. doi: 10.3982/ECTA14281

Crossref Full Text | Google Scholar

FES (2021a). Youth and social justice Almaty. Available online at: https://library.fes.de/pdf-files/bueros/kasachstan/18532.pdf (Accessed August 10, 2025).

Google Scholar

FES. (2021b). Central Asian, Eastern and Southeastern European youth survey 2018/2019 and 2020/2021. Friedrich-Ebert-Stiftung Kazakhstan. Almaty. Available online at: https://library.fes.de/pdf-files/bueros/kasachstan/18532.pdf

Google Scholar

Gallup (2022). Inside Kazakhstan: record low confidence in government. Available online at: https://news.gallup.com/poll/388658/inside-kazakhstan-record-low-confidence-government.aspx (Accessed August 10, 2025).

Google Scholar

Granovetter, M. (1978). Threshold models of collective behavior. Am. J. Sociol. 83, 1420–1443. doi: 10.1086/226707

Crossref Full Text | Google Scholar

Groves, R. M., and Peytcheva, E. (2008). The impact of nonresponse rates on nonresponse bias: a meta-analysis. Public Opin. Q. 72, 167–189. doi: 10.1093/poq/nfn011

Crossref Full Text | Google Scholar

Gulin, K. A. E., and Dementieva, I. N. (2011). Protest sentiments of the region’s population in crisis. Econ. Soc. Chang. 3, 46–57. Available at: http://www.vscc.ac.ru/files/journal/issues/issue-esc-15-2011-3-en.pdf#page=46

Google Scholar

Ibadildin, N., and Primiano, C. (2024). Understanding ‘bloody January’2022: a ‘limited access order’in Kazakhstan. Asian Aff. 55, 623–647. doi: 10.1080/03068374.2024.2427371

Crossref Full Text | Google Scholar

Jasper, J. M. (2014). Protest: a cultural introduction to social movements. New Jersey: John Wiley & Sons.

Google Scholar

Junisbai, B., and Junisbai, A. (2020). Are youth different? The Nazarbayev generation and public opinion in Kazakhstan. Probl. Post Communism 67, 251–263. doi: 10.1080/10758216.2018.1520602

Crossref Full Text | Google Scholar

Kilybayeva, S., and Ibadildin, N. (2023). “The shift in Kazakhstan citizens’ political participation: pre and post the 2019 political transition” in Producing cultural change in political communities: the impact of populism and extremism on the international security environment (Cham: Springer Nature Switzerland), 103–122.

Google Scholar

King, G., Pan, J., and Roberts, M. E. (2013). How censorship in China allows government criticism but silences collective expression. Am. Polit. Sci. Rev. 107, 326–343. doi: 10.1017/S0003055413000014

Crossref Full Text | Google Scholar

KISI (2022) “For people about people”: which political institutions do Kazakhstani citizens trust. Available online at: https://kisi.kz/wp-content/uploads/2023/08/05-03-2022.pdf

Google Scholar

Krumpal, I. (2013). Determinants of social desirability bias in sensitive surveys. Qual. Quant. 47, 2025–2047. doi: 10.1007/s11135-011-9640-9

Crossref Full Text | Google Scholar

Kudaibergenova, D. T. (2022). Art and protest in Kazakhstan. Curr. Hist. 121, 271–276. doi: 10.1525/curh.2022.121.837.271

Crossref Full Text | Google Scholar

Kudaibergenova, D. T., and Laruelle, M. (2022). Making sense of the January 2022 protests in Kazakhstan: failing legitimacy, culture of protests, and elite readjustments. Post-Sov. Aff. 38, 441–459. doi: 10.1080/1060586X.2022.2077060

Crossref Full Text | Google Scholar

Kuran, T. (1995). Private truths, public lies: the social consequences of preference falsification. Cambridge, Massachusetts: Harvard University Press.

Google Scholar

Lohmann, S. (1994). The dynamics of informational cascades: the monday demonstrations in Leipzig. World Polit. 47, 42–101. doi: 10.2307/2950679

Crossref Full Text | Google Scholar

McAdam, D., Tarrow, S., and Tilly, C. (2001). Dynamics of contention. Cambridge, United Kingdom: Cambridge University Press.

Google Scholar

Mukhamediyev, B., Bimendiyeva, L., Dauliyeva, G., and Temerbulatova, Z. (2023). Unrest in Kazakhstan: economic background and causes. Cogent Econ. Finance 11:2263305. doi: 10.1080/23322039.2023.2263305

Crossref Full Text | Google Scholar

Pickard, S. (2019). “Young people, protest and dissent” in Politics, protest and young people: Political participation and dissent in 21st century Britain. ed. S. Pickard (London: Palgrave Macmillan UK), 407–444.

Google Scholar

Robertson, G. B. (2011). The politics of protest in hybrid regimes: Managing dissent in post-communist Russia. Cambridge, United Kingdom: Cambridge University Press.

Google Scholar

Sawyer, P. S., Romanov, D. M., Slav, M., and Korotayev, A. V. (2022). Urbanization, the youth, and protest: a cross-national analysis. Cross-Cult. Res. 56, 125–149. doi: 10.1177/10693971211059762

Crossref Full Text | Google Scholar

Sheryazdanova, G., Nurtazina, R., Byulegenova, B., Serikzhanova, A., and Dzhunusova, A. (2024). Socioeconomic and political causes of youth radicalization during the January 2022 events in Kazakhstan. Global Perspect. 5:120495. doi: 10.1525/gp.2024.120495

Crossref Full Text | Google Scholar

StatCounter Global Stats. (2025). Mobile social media stats Kazakhstan. Available online at: https://gs.statcounter.com/social-media-stats/mobile/kazakhstan (accessed August 19, 2025)

Google Scholar

Steinert-Threlkeld, Z. C. (2017). Spontaneous collective action: peripheral mobilization during the Arab spring. Am. Polit. Sci. Rev. 111, 379–403. doi: 10.1017/S0003055416000769

PubMed Abstract | Crossref Full Text | Google Scholar

Tarrow, S. (2011). Power in movement: Social movements and contentious politics. 3rd Edn. Cambridge, United Kingdom: Cambridge University Press.

Google Scholar

Ternov, N., Nurtazina, R., and Serikzhanova, A. (2024). The sociopolitical January 2022 protests in Kazakhstan’s telegram channels: agenda interception. Glob. Perspect. 5:120497. doi: 10.1525/gp.2024.120497

Crossref Full Text | Google Scholar

Tourangeau, R., and Yan, T. (2007). Sensitive questions in surveys. Psychol. Bull. 133, 859–883. doi: 10.1037/0033-2909.133.5.859

PubMed Abstract | Crossref Full Text | Google Scholar

Troitskiy, E. F., Pogorelskaya, A. M., Yun, S. M., Dunaeva, D. O., and Blaginin, A. L. (2024). Kazakhstan’s January 2022 crisis: representations by national experts via social media. J. Eurasian Stud. 15, 227–238. doi: 10.1177/18793665231212575

Crossref Full Text | Google Scholar

Tufekci, Z. (2017). Twitter and tear gas: the power and fragility of networked protest. New Haven, Connecticut: Yale University Press.

Google Scholar

Ustyuzhanin, V. V., Sawyer, P. S., and Korotayev, A. V. (2023). Students and protests: a quantitative cross-national analysis. Int. J. Comp. Sociol. 64, 375–401. doi: 10.1177/00207152221136042

Crossref Full Text | Google Scholar

Vanhuysse, P. (2006). Divide and pacify: strategic social policies and political protests in post-communist democracies. Budapest: Central European University Press.

Google Scholar

Wang, Y. (2022). Falsification of ‘manufactured compliance’and wider legitimation and governmentality issues: An Pension policy and governmentality in China: Manufacturing public compliance. London: LSE Press. Available at: https://doi.org/10.31389/lsepress.ppc.f.

Google Scholar

World Bank (2022). Listening to Kazakhstan. information on socio-economic well-being. World bank poverty and equity global practice. Available online at: https://www.vsemirnyjbank.org/ru/country/kazakhstan/brief/l2kaz#3

Google Scholar

Youth. (2019). Sociological study “youth of Kazakhstan”. Scientific Research Center Youth, Nur-Sultan. 94. Available online at: https://eljastary.kz/upload/iblock/a64/a64cb0fb812ebaeb34a9debc4e54bfd6.pdf

Google Scholar

Youth. (2020). Sociological study “youth of Kazakhstan”. Scientific Research Center Youth, Nur-Sultan. 146. Available online at: https://eljastary.kz/upload/iblock/202/2022852ac194f9b97058aa7a7d4199bf.pdf

Google Scholar

Youth. (2021). Sociological study “youth of Kazakhstan”. Scientific Research Center Youth, Nur-Sultan. 103. Available online at: https://eljastary.kz/upload/iblock/09f/myu1r796xa7yrauvd107iow2ei1fq332.pdf

Google Scholar

Youth. (2022). Sociological study “youth of Kazakhstan”. Scientific Research Center Youth, Astana. 129. Available online at: https://eljastary.kz/ru/research/18909

Google Scholar

Youth (2023). Sociological study “youth of Kazakhstan”. Astana: Scientific Research Center Youth, 137 https://eljastary.kz/upload/iblock/830/dygffgydxa2x0ndoesilqkd4lel2jk2a.pdf.

Google Scholar

Youth (2024). Sociological study “youth of Kazakhstan”. Scientific Research Center Youth, Astana. 104. Available online at: https://eljastary.kz/upload/iblock/3d2/90lx4begmhrsrgaen53cfakrxamzezi8.pdf

Google Scholar