Ying Hu
Hongfei Li- College of Foreign Languages, Ocean University of China, Qingdao, China
U.S.-China trade tensions have reshaped global economic relations and produced a discursive struggle over identity, threat, and legitimacy. While research in Critical Discourse Analysis (CDA) and Critical Cognitive Linguistics (CCL) has examined ideological framing, few studies have systematically modeled how diplomatic discourse constructs shifting representations over time. This study proposes the Context-Deictic Space Model (CDSM), a socio-cognitive framework integrating van Dijk’s Context Model with Chilton’s Deictic Space Theory. By mapping participants, settings, and events onto spatial, temporal, and axiological axes, CDSM visualizes ideological positioning in discourse. Applied to three U.S. trade policy agendas (2017–2019), the analysis shows how China is reframed from a distant trade partner to a proximate adversary, invoking crisis and legitimizing protectionism while marginalizing actors like the World Trade Organization (WTO). Theoretically, the study extends CCL by offering a visualizable model of ideological distance; empirically, it provides a new lens for analyzing threat construction in political discourse.
1 Introduction
The U.S.-China trade conflict, which escalated in 2018 under the Trump Administration, has emerged as one of the most consequential economic and geopolitical disputes of the 21st century (Turner, 2022). Triggered by U.S. tariffs on Chinese imports and subsequent retaliatory measures from Beijing, the confrontation has significantly reshaped global trade dynamics (Bown, 2021). As geopolitical rivalry between the two powers deepens, so too does the need to understand how language constructs economic threats, justifies policy decisions, and mobilizes public opinion.
Accordingly, over the past two decades, discourse surrounding U.S.-China trade relations has attracted increasing scholarly attention across fields such as linguistics, communication, international studies, politics, and sociology (Lu, 2018; Boylan et al., 2021; Lawrence et al., 2021; Liu and Huang, 2024; Qu et al., 2024). Linguistic studies have primarily focused on news and media discourse, while relatively less attention has been paid to diplomatic discourse such as trade policy agendas. This study aims to fill that gap by analyzing how China was represented in U.S. trade policy agendas issued during the first Trump Administration, with particular attention to changes before and after the outbreak of the U.S.-China trade war in 2018. As Jin (2007, p. 21) notes, diplomatic discourse is “used by sovereign states to communicate their international strategies and foreign policies in a certain historical period.” The U.S. trade policy agenda, in this regard, serves as one of the most authoritative channels through which the government articulates its diplomatic positions and trade strategies.
In the fields of communication and linguistics, many scholars analyze U.S.-China trade discourse using Critical Discourse Analysis (CDA), an approach that views language as a form of social practice shaped by power and ideology (Wodak and Meyer, 2015; Li, 2021; Ng, 2021; Zhang et al., 2023). Studies employing CDA typically focus on lexical choice, thematic framing, and rhetorical strategy in political and media texts, often using qualitative textual analysis to uncover ideological presuppositions (Blanchard, 2013; Hinck, 2017). However, such approaches are sometimes critiqued for “cherry-picking” (Baker, 2006; Baker et al., 2008) and for lacking systematic tools to trace how discursive representations of ‘Self’ and ‘Other’ evolve across time or documents. To address this, recent scholarship increasingly incorporates corpus linguistics into CDA to enhance empirical rigor (Li, 2009; Chen and Wang, 2022; Fu and Wang, 2022; Zhang et al., 2023; Xu, 2025). Nevertheless, existing work still rarely models the cognitive mechanisms through which ideological positioning is constructed and reconfigured in official policy discourse.
To overcome these limitations, this study draws on Critical Cognitive Linguistics (CCL) to examine how language both reflects and shapes social cognition (Hart and Lukeš, 2009; Hart, 2017). CCL is particularly useful for analyzing how ideological representations of ‘Self’ and ‘Other’ are constructed and sustained over time. Building on this theoretical foundation, the study proposes the Context-Deictic Space Model (CDSM)—an original framework that integrates context model with deictic space theory. This hybrid model enables a more systematic, visualizable analysis of how political discourse positions China ideologically and cognitively within U.S. trade policy agendas.
Theoretically, CDSM extends the socio-cognitive strand of CCL by concretizing abstract social cognition within a spatialized mental model, and complements proximization-based approaches by embedding ideological distance within broader contextual structures. Situated within linguistic research on political discourse, this study contributes to discourse analysis and cognitive linguistics. In practical terms, it sheds light on how trade policy language constructs adversarial identities and legitimizes strategic choices, offering timely insight into the communicative dynamics underpinning U.S.-China economic tensions.
2 Literature review
Critical Cognitive Linguistics (CCL) is an interdisciplinary framework that integrates insights from Critical Discourse Analysis (CDA) and Cognitive Linguistics (CL) to explore how language reflects and constructs social reality (Koller, 2004; Hart and Lukeš, 2009; Núñez-Perucha, 2011; Hart, 2017). Rather than viewing discourse as a mirror of objective conditions, CCL emphasizes its role in shaping ideological understanding through cognitive operations. Key concepts in this approach include image schemas, conceptual metaphors, and discourse space (Lakoff and Johnson, 1980, 2008; Fauconnier, 1994; van Dijk, 2001, 2006; Fauconnier and Turner, 2002; Musolff, 2003; Charteris-Black, 2004, 2006; Chilton, 2004; Cap, 2008), which together explain how individuals mentally organize and interpret social experience through language (Hart, 2011). This study focuses particularly on two underutilized but conceptually significant models within the CCL framework: van Dijk’s context model and Chilton’s Deictic Space Model (DSM). These models offer complementary strengths for analyzing how discourse shapes social cognition and ideological positioning.
2.1 Context model
The context model, a key component of van Dijk’s socio-cognitive approach, originates from the situation model in cognitive psychology (Johnson-Laird, 1983). While situation models represent immediate mental processing of discourse in short-term memory, the context model abstracts and generalizes multiple situation models into a cohesive structure stored in long-term memory (van Dijk and Kintsch, 1983), thereby connecting discourse processing with broader social cognition. This model integrates individual experiences with shared social beliefs and knowledge, making it particularly relevant for analyzing political discourse, where ideology and group positioning are often foregrounded.
The context model is hierarchically organized into three primary categories: Setting, Participants, and Events, each comprising various subcategories (van Dijk, 2008, p. 76). The “Setting” includes spatial–temporal and institutional dimensions, while “Participants” encompasses identities, roles, and intentions, and “Events” refer to the nature and trajectory of discourse-relevant actions. Crucially, the model is egocentric, with ‘Self’ as its central category, organizing relationships between the discourse producer (e.g., speaker or writer) and other participants. This self-centered structure reflects the producer’s intentions to manipulate cognitive representations and emotions, aligning with the ideological focus of CDA. This makes the context model especially valuable for analyzing how political actors construct an “us-versus-them” narrative, and how discourse producers encode evaluative stances toward allies and adversaries.
Although the context model provides a theoretically robust framework for examining how discourse relates to social cognition, its application remains limited in empirical studies—especially in critical cognitive linguistics—due to its abstract and macro-level nature. Most CDA and CCL research continues to focus on micro-level phenomena such as lexical choices and metaphorical framing, often overlooking how these are embedded within more stable, socially shared contextual schemata. Moreover, few studies have operationalized the context model in a way that allows for dynamic visualization or systematic comparison across multiple documents (e.g., policy agendas or political speeches). This is a research gap, particularly in analyzing evolving political discourse, such as the shifting representation of China across U.S. trade policy discourse.
Some recent attempts have begun to engage with the context model in political discourse (e.g., Wodak and Meyer, 2015; van Dijk, 2008). Kaufova and Kaufova (2015) adopt context model as a socio-cognitive method applied to David Cameron’s speech at the Conservative Party’s annual conference. Abdel-Raheem (2020) suggests that visual and multimodal discourse will benefit from adopting van Dijk’s mental models. However, these applications largely remain general and abstract, lacking concrete tools to visualize how the dynamic ‘Self-Other’ relations evolve across time and across multiple texts. To address these limitations, this study seeks to integrate the context model into a cognitively grounded mental space, enabling a more detailed and comprehensive analysis of political discourse.
2.2 Deictic space model
The Deictic Space Model (DSM), developed by Chilton (2004) and Chilton (2010) as an extension of Discourse Space Theory, provides a spatial framework for representing discourse entities along three axes: spatial (S), temporal (T), and evaluative (A). Anchored at the speaker’s deictic center—“here,” “now,” and “good”—DSM visualizes how speakers position” Self” and “Other” in discourse, often to construct ideological proximity or distance.
While DSM is effective in mapping static relationships, it has limitations in capturing dynamic movements of discourse entities, especially across larger discourse structures. To address this, Cap’s (2014, 2017, 2020) Proximization Theory introduced a dynamic component, analyzing how perceived threats or allies shift toward or away from the speaker over time.
Recent years have seen a growing body of literature applying the Discourse Space Model (DSM) and its derivative, Proximization Theory, to various domains of discourse, including political discourse, media discourse, and environmental discourse. Most existing DSM studies are employed in political discourse (Wang, 2019). For instance, Cap (2018) applies Proximization theory to polish anti-immigration discourse, analyzing how emerging ideological and physical threats are framed to justify coercive state policies. Similarly, Cervi et al. (2020) examine Matteo Salvini’s populist rhetoric, revealing how proximization of the in-group and distanciation of immigrants and NGOs serve to legitimize xenophobic agendas through symbolic boundary work. Ma and Wen (2023) extend Chilton’s original DST by incorporating an alternative spatial construal to differentiate between cooperative and conflictual discourse. They demonstrate that cooperative political rhetoric tends to construct an outward-extensive discourse space, while antagonistic discourse typically contracts toward the speaker’s deictic center. Building on these insights, Gao and Sun (2024) propose a “Symbolic Distance Adjusting” framework that integrates both proximization and distanciation. Their analysis of President Tokayev’s 2022 address highlights how dual manipulation of symbolic distance supports both in-group legitimization and out-group delegitimization, thus enriching the cognitive toolkit of Critical Discourse Studies. Mammadov and Mammadov (2019) contribute further by examining how the conceptual dimensions of time, space, and person interact to shape perceptions in political discourse. Their work emphasizes the role of proximization and directionality as linguistic resources for contextualizing social meaning and ideological stance.
DSM and proximization have also been productively applied to media and transcultural discourse contexts. Chen et al. (2020), for example, adopt proximization theory to compare the Sino–US trade war discourse in Weibo and Twitter posts by Xinhua News Agency. Their findings show how China proximized itself as an economic protagonist while tailoring spatial–temporal-axiological proximization to different audiences to enhance political legitimacy. Kowalski (2022) explores proximization in the dialogic exchanges between journalists and readers in Polish and Romanian media. His study identifies strategies such as categorization, historical analogy, and hybrid discourse space construction, demonstrating how ideological positions are negotiated in online public spheres. A multimodal approach is adopted by Porto and Belmonte (2025), who analyze European news coverage of the Brazilian Congress attack. Her study reveals how framing and construal strategies proximize Bolsonaro’s supporters as a threat while aligning audiences with Lula’s government, illustrating how media narratives shape social polarization.
Beyond political discourse, DSM and Proximization have proven valuable in other domains. Kader (2024) conducts a linguistic analysis of a TED Talk on plastic pollution, showing how spatial, temporal, and axiological proximization is employed to frame environmental risk and mobilize audience concern. Their study highlights the persuasive potential of proximization in environmental advocacy. Ye and Chen (2023) extend the model’s application to telecommunication fraud, demonstrating how fraudsters utilize proximization to manipulate victims’ perception of threat, urgency, and trust. This work exemplifies how proximization can function in deceptive as well as persuasive discourse beyond institutional or political settings.
However, most existing applications remain at the micro-level—focusing on verbs or noun phrases—while offering limited insight into broader ideological shifts across texts or time. Furthermore, DSM alone does not fully incorporate contextual dimensions such as institutional setting or shared social cognition.
These gaps suggest the need for a more integrated and scalable framework that can account for both spatial positioning and contextual embedding of discourse entities over time.
2.3 Previous studies on US-China trade discourse
U.S.-China trade conflict has attracted sustained attention from scholars across disciplines, particularly in international relations and media studies. These studies typically examine the geopolitical motivations, shifting power relations, and ideological tensions embedded in bilateral economic discourse (e.g., Ooi and D’Arcangelis, 2017; Wang and Zeng, 2020; Cheng and He, 2022). Media scholars have further explored how divergent national narratives are constructed through framing and agenda-setting in journalistic texts (Zhang, 2022; Qin and Zhang, 2020), revealing competing ideologies and public opinion strategies in both Chinese and Western media.
Within linguistics, a growing body of research has examined how language shapes, reflects, and mediates the dynamics of the China–US trade dispute. These studies adopt various analytical frameworks, including Critical Discourse Analysis (CDA), frame analysis, corpus-assisted discourse studies (CADS), metaphor studies, multimodal analysis, and appraisal theory.
A number of studies have adopted Critical Discourse Analysis (CDA) to reveal underlying ideologies in trade-related discourse (Wang and Ge, 2020). For instance, Zhou and Qin (2020) analyze The New York Times coverage of China’s tariff actions, demonstrating how lexical and grammatical choices construct a negative image of China aligned with American political interests. Li (2020), using Hallidayan grammar and Fairclough’s model, compares China Daily, The New York Times, and The Guardian, revealing distinct ideological orientations—cooperation in Chinese media, critique in British media, and ambivalence in American coverage.
Frame-based approaches have also been applied. Zhu (2022) identifies how Chinese official discourse frames the trade dispute using culturally resonant metaphors (e.g., “journey,” “cooperation”) to foster national identity and legitimize governmental stances. Similarly, Tang (2023) shows how Chinese diplomats use discursive strategies such as intertextuality and recontextualization to counter negative portrayals in British newspapers.
Corpus-based approaches have provided broader empirical coverage. Liu (2017) compares the representation of the currency dispute in China Daily and The New York Times, showing that both reflect neoliberal logics, though the NYT uses them to justify assertiveness, while CD remains ambivalent. Liu and Huang (2024), analyzing 14 U.S. and 33 Chinese economic texts, report lexical and thematic variations that reflect divergent economic priorities and ideological framing.
Metaphor studies constitute another major strand. Cai and Deignan (2019) find both Chinese and British newspapers draw on “war” metaphors in trade coverage, but with different evaluative orientations—Chinese texts depict protectionism as a threat, while United Kingdom texts frame free trade as a victim. Tan et al. (2024) trace metaphor shifts across U.S.–China discourse from the Clinton to the Trump era, revealing changes in ideological stance and power asymmetries. Multimodal and appraisal-based studies further enrich the landscape. Zhang and Forceville (2020) examine political cartoons from both countries and show that while similar metaphorical themes recur (e.g., trade as battle), the visual framing and metonymic cues vary in ways that reflect cultural and political context. Qin and Zhang (2020), applying Appraisal Theory, compare news headlines on the trade war and demonstrate how stance is mediated differently through translation: Chinese media present China more positively and the U.S. more negatively, often using heteroglossic resources, while U.S. headlines tend to be monoglossic.
A small number of studies have focused on diplomatic discourse. Cheng (2021) applies van Leeuwen’s legitimation framework to Chinese white papers, identifying authorization, rationalization, and moral evaluation as key strategies. These are shown to draw upon Confucian and collectivist values to justify China’s actions. Zhu (2022) and Tang (2023) similarly highlight how Chinese media and diplomats make strategic use of cultural symbols and narrative frames to shape international perception. Social media discourse also provides insight into public reception. Bouvier et al. (2024) analyze Weibo posts during the 2021 U.S.–China Alaska talks, finding that nationalism—not xenophobia—dominates public response. Users frame the event as part of a longer struggle against historical humiliation, reinforcing pride in China’s current diplomatic strength.
Despite the breadth of analytical frameworks and empirical domains reviewed above, most existing studies focus on surface-level textual features—such as metaphor, lexical choice, and thematic framing, without systematically accounting for the underlying cognitive mechanisms that organize these meanings across time and across genres. While critical discourse studies have effectively demonstrated ideological positioning, they often lack tools to track how such positions shift within and across policy discourse. Notably, few studies examine how the representation of China evolves over time in official U.S. trade documents, or how this evolution is constructed through spatial, temporal, and evaluative deixis anchored in speaker perspective.
Furthermore, existing models tend to isolate either spatial deixis (as in DSM and Proximization Theory) or context-driven cognition (as in van Dijk’s context model), but rarely integrate both in a way that enables dynamic, comparative analysis across sequential texts. This gap limits our understanding of how politicians or discourse producers use discourse not only to frame policy but to reconfigure ideological relationships through shifting self–other positioning.
To address these gaps, this study proposes the Context-Deictic Space Model (CDSM), a novel analytical framework that integrates van Dijk’s context model with Chilton’s deictic space model. By combining spatial, temporal, and axiological positioning with socially shared context schemata, CDSM offers a scalable and cognitively grounded method for visualizing how trade policy discourse constructs evolving representations of China within the U.S. diplomatic agenda.
3 Context-deictic space model
This chapter introduces the Context-Deictic Space Model (CDSM), which is proposed as a synthesis of two existing frameworks—van Dijk’s context model and Chilton’s Deictic Space Model (DSM). Drawing on insights from Critical Cognitive Linguistics (CCL), this model aims to visualize the dynamic interplay between discourse, cognition, and ideology in political texts.
3.1 Conceptual foundations: context model and deictic space model
The Context-Deictic Space Model (CDSM) proposed in this study builds on two foundational frameworks: van Dijk’s context model and Chilton’s Deictic Space Model (DSM). The context model conceptualizes how discourse interacts with social cognition, particularly in shaping ideologies, intentions, and contextual appropriateness (see Section 2.1). The DSM visualizes how ideological distance and proximity between the self and the other are constructed through spatial, temporal, and evaluative dimensions (see Section 2.2). Each model contributes distinct yet complementary strengths: while the context model captures cognitive abstraction and sociocultural embedding, the DSM offers a visual grammar for tracking spatial, temporal, and axiological proximity.
Figures 1, 2 briefly illustrate the basic structure of these two models, which serve as the conceptual foundation for the construction of the integrated CDSM in the next section.
Figure 1. Categorical schemata for the context model (van Dijk, 2008, p. 76).
Figure 2. Deictic space model (Chilton, 2010).
3.2 Construction of the context-deictic space model
Before detailing the construction process, it is important to clarify that the CDSM discussed in this study refers to an idealized model, designed to abstract away individual differences in readers’ knowledge and experience to focus on shared social cognition within a given social group. This ideal model reflects the intentions and cognitive predictions of the discourse producer, whose goal is to manipulate the audience’s perceptions through discourse. By visualizing this dynamic, the ideal model provides two critical contributions: it makes abstract social cognition more tangible and it explicitly captures the discourse producer’s strategy to influence social attitudes and beliefs. In the ideal CDSM, the deictic center represents the discourse producer’s defined position in space, time, and reality (s0, t0, a0). Entities are positioned relative to the deictic center in either the Setting Zone or the Event Zone, depending on their temporal and cognitive proximity. This positioning influences how readers perceive and evaluate these entities, shaping their social cognition and, by extension, their interaction with broader social structures. Conversely, the social structure itself also shapes the discourse producer’s attitudes and decisions in positioning entities, creating a reciprocal dynamic between discourse and society.
The construction of the CDSM involves projecting the core categories of the context model onto the three-dimensional deictic space defined by the spatial (s), temporal (t), and axiological (a) axes (Figure 3). The deictic space is divided into two primary zones: the Setting Zone, which represents past events or conditions, and the Event Zone, which focuses on future events or goals. The Setting Zone (s,−t,a) corresponds to the context model’s Setting category and integrates spatial, temporal, and evaluative dimensions. The Event Zone (s,+t, a) corresponds to the Event category, with discourse entities positioned based on their temporal and axiological proximity to the deictic center.
Figure 3. Construction of context-deictic space model.
Participants in the discourse are projected differently across these zones based on their roles and relationships to the deictic center. For instance, Circumstances are typically mapped to the Setting Zone along the negative t-axis, while Actions are projected into the Event Zone along the positive t-axis, often pointing toward Goals located farther along the same axis. This configuration allows for a comprehensive spatial representation of discourse entities and processes.
At the linguistic level, the CDSM is constructed through a systematic process: discourse entities are first identified and categorized into ‘Self’ (proximal to the deictic center) and ‘Other’ more distant along the s-axis. Events and processes are then classified by their temporal orientation and mapped to either the Setting Zone or the Event Zone based on linguistic markers such as tense and temporal adverbs. Finally, evaluative judgments are incorporated using modal verbs, negative constructions, and other expressions that indicate degrees of possibility or desirability, placing entities along the a-axis.
The Event Zone is more salient to readers than the Setting Zone, as entities closer to the deictic center tend to align with the producer’s perspective and evoke stronger identification or opposition. Discourse entities positioned at the far ends of the s- and a-axes, particularly in the Setting Zone, are more likely to be marginalized or excluded. However, entities positioned at the far s-axis but proximal a-axis may represent threats or adversaries perceived as “real” in the objective world, highlighting their role in shaping ideological conflicts.
Thus, the CDSM transforms the abstract categories of the context model into a concrete, visualized framework, linking linguistic structures to broader social and cognitive processes.
3.3 Strengths of the context-deictic space model
The Context-Deictic Space Model offers significant improvements over its predecessors. First, it enhances the operability of the context model by systematically abstracting and accumulating semantic information from multiple situation models. This process ensures that the spatial positions of discourse entities remain stable and consistent, making the analysis more reliable. Second, the model is not limited to small linguistic units, such as words or phrases, but can also accommodate larger units, such as sentences or entire texts, thereby broadening its applicability to macro-level discourse analysis.
Furthermore, the CDSM accounts for the dynamic movement of discourse entities, a limitation of the original DSM. By allowing participants to occupy different positions in both the Setting and Event Zones within a single model, the CDSM effectively captures changes in entity roles or relationships over time. This feature is particularly useful for analyzing political discourse, where shifts in ideological framing are common.
In combining the context model and DSM, the CDSM not only addresses the static limitations of the DSM but also visualizes the abstract nature of the context model. This integration provides a novel approach to understanding the interaction between discourse, social cognition, and ideology. By revealing the spatial dynamics of ideological confrontations, the CDSM deepens our understanding of how political discourse influences and is influenced by broader social structures, offering new insights for critical cognitive linguistics.
4 Data and methods
4.1 Data
This study analyzes three Trade Policy Agendas released during the first 3 years of the Trump presidency (2017, 2018, and 2019). These agendas are examined to uncover how the administration’s discourse on China evolved during this period. To ensure the accuracy and reliability of the data, the researchers identify all sentences explicitly referencing “China” or “Chinese” within each agenda. In addition, implicit references to China are manually extracted through a comprehensive reading of the texts. To minimize individual bias, two additional researchers with expertise in Critical Discourse Studies (CDS) and Cognitive Linguistics (CL) independently perform the same extraction process. Sentences included in the final corpus are those unanimously selected by all researchers, while any contentious sentences are excluded. The exclusion rates were low across 3 years: 0% in 2017, 0.99% in 2018, and 1.96% in 2019, indicating strong coder agreement and a representative dataset.
The study uses AntConc to analyze the frequency and context of terms such as “China” or “Chinese.” Table 1 presents detailed data on the China-related content in the Trade Policy Agendas. The results show that China-related content increased significantly over the 3 years, both in terms of absolute word count and percentage of total content. In 2017, China-related sentences accounted for only 4.4% of the agenda’s total word count, but this figure rose to 10.3% in 2018 and 17.8% in 2019. The ranking of “China” in the wordlist of each agenda also climbed dramatically, moving from 120th in 2017 to 16th in 2019. These trends reflect the increasing focus on China in the Trump Administration’s trade rhetoric, suggesting a deliberate effort to foreground China as a central issue in U.S. trade policy.
Table 1. China-related sentences in Trump Administration’s trade policy agendas.
4.2 Methods
This study employs the Context-Deictic Space Model (CDSM), an integrated framework combining elements of the Context Model and the Deictic Space Model to analyze the China-related sentences extracted from the trade policy agendas. The analytical process unfolds in several stages. First, discourse entities and events are identified from the text based on situation models, with all context-related semantic information extracted and categorized into the hierarchical structure of the context model. This involves organizing data into three main categories: Participants, representing entities such as the U.S., China, or the WTO; Setting, encompassing the spatial, temporal, and environmental aspects of the discourse; and Events, which refer to the actions or processes described.
Next, the categories of the context model are projected into the three-dimensional Deictic Space, which is defined by spatial (s), temporal (t), and axiological (a) axes. This step visualizes the abstract social cognition encoded in the discourse, making it possible to analyze the positioning of China-related entities within the mental space of the reader. The spatial axis reflects the conceptual distance between the ‘Self’ (e.g., the U.S.) and the “Other” (e.g., China), the temporal axis distinguishes past, present, and future events, and the axiological axis evaluates the proximity of entities to the discourse producer’s perspective.
Finally, the Context-Deictic Space Model is used to conduct a critical cognitive analysis of the discourse entities and events. This analysis focuses on uncovering the ideological underpinnings of the Trump Administration’s trade rhetoric, examining how China is framed in relation to the U.S., and identifying strategies such as foregrounding and backgrounding used to influence public perception. These procedures align with the three-dimensional research paradigm of Critical Discourse Analysis, moving from the description of linguistic features, to the interpretation of social cognition, and ultimately to the explanation of broader social structures and power dynamics.
By integrating the strengths of the Context Model and the Deictic Space Model, this method offers a nuanced approach to analyzing political discourse, revealing not only the explicit content of the agendas but also the implicit social and ideological structures shaping them.
5 The CDSM of Trump Administration’s trade policy agendas
This section applies the proposed Context-Deictic Space Model (CDSM) to analyze the representation of China in the U.S. trade policy agendas released during the Trump Administration (2017–2019). The CDSM is used to visualize how discourse entities such as “China,” “the U.S.,” and the “WTO” are positioned within the spatial, temporal, and axiological dimensions of the agendas, revealing the socio-cognitive strategies employed by the discourse producer.
The case study proceeds by identifying key discourse entities and their spatial relations within the agendas, focusing on shifts in China’s position across the three texts. Each agenda is analyzed in relation to the historical and political context of its publication, using the CDSM to map ideological and geopolitical narratives.
5.1 Context-deictic space model of China-related 2017 agenda
Compared to the subsequent agendas, the 2017 Trade Policy Agenda contains relatively limited China-related content. This Agenda is set against the backdrop of a declining U.S. foreign trade landscape and the simultaneous rise of China over the past three decades.
Key discourse entities and events identified in the 2017 Agenda include “the U.S.,” “trade growth of the U.S.,” “the great trading system for the U.S.,” and “the great trading system for China” (see Table 2). Among these, “trade growth of the U.S.” and “the great trading system for the U.S.” are classified under the ‘Self’ group, while “the great trading system for China” is assigned to the ‘Other’ group. Accordingly, in the Context-Deictic Space Model, “trade growth of the U.S.” and “the great trading system for the U.S.” are positioned close to the deictic center along the spatial (s) axis, whereas “the great trading system for China” is situated at the distal end of the s-axis, reflecting its alignment with the ‘Other.’
Table 2. Construction of discourse entities in 2017 Agenda.
These discourse entities are mapped into either the Setting or Event zones within the Context-Deictic Space, with their positions along the temporal (t) axis determined by their tense, adverbs, or temporal references. For instance, the entity “trade growth of the U.S.” is frequently expressed in the past tense, symbolizing the country’s earlier economic success. Consequently, this entity is placed at the distal end of the t-axis, indicating its association with historical events.
The participants within the context model are identified as the United States, China, and the World Trade Organization (WTO).
In the Context-Deictic Space Model of the China-related 2017 Trade Policy Agenda (hereafter referred to as the 2017 model) (see Figure 4), the opposition between the United States and China—captured through the contrast of the “great trading system for the U.S.” versus the “great trading system for China”—is positioned within the Event zone. This opposition is situated proximally to the deictic center along the temporal (t) axis, signifying its immediate relevance in the narrative. The 2017 Agenda frequently contrasts how the global trading system and the WTO have benefited China while failing to generate comparable outcomes for the United States. For instance, situations (5) and (7) highlight these disparities, framing the system as favoring China at the expense of U.S. interests.
Figure 4. Context-deictic space model of China-related 2017 trade policy agenda.
After the accumulation and abstraction of Situation Models related to ‘the great trading system for China’, in the Context-deictic Space Model, this discourse entity is mapped in the Context-Deictic Space Model close to the deictic center along the axiological (a) and temporal (t) axes but distant on the spatial (s) axis. In contrast, the “great trading system for the U.S.” is placed farther from the deictic center on the a-axis but nearer on the s-axis, categorizing it as part of the unrealistic ‘Self’ group. The contradiction between these two entities underscores the narrative of China’s unfair gains in trade being facilitated by an inefficient WTO, portrayed as an organization exerting significant influence on global trade dynamics.
The 2017 Agenda also adopted plenty of situations to describe the glorious past of US foreign trade. For example, situation (4) notes that from 1984 to 2000, U.S. industrial production increased by almost 71 percent. The use of past tense here emphasizes that such growth is now a thing of the past, unattainable in the current global trade environment. In the Context-Deictic Space Model, this past prosperity, belonging to the ‘Self’ group, is positioned near the deictic center along the s-axis and a-axis but distant on the t-axis, reflecting its temporal detachment from the present. This past prosperity forms a stark contrast to the current global trading system that benefits China, further reinforcing the Agenda’s critical stance.
In summary, the 2017 model represents China’s position through the discourse entity “great trading system for China,” emphasizing the perceived unfairness of the global trading system. The Sino-U.S. relationship is depicted indirectly in the Event zone, cultivating a sense of crisis in the reader’s mental space. Meanwhile, the narrative surrounding the U.S.’s past trade achievements is backgrounded, creating a sense of alienation and nostalgia, which impacts social cognition. This rhetorical strategy seeks to garner public support for the newly inaugurated Trump Administration by projecting a narrative of loss and unfairness in U.S. trade practices. The emphasis on an “unfair trade system” and alleged U.S. trade losses appeals to the cognitive dimension of the general public, particularly American citizens, fostering trust in the Administration’s trade agenda. In this way, the discourse effectively manipulates public perception to enhance confidence in the Trump Administration’s leadership.
5.2 Context-deictic space model of China-related 2018 agenda
Following discourse entities or events involved in the 2018 Agenda are identified manually: China’s unfair trade practices, efforts of US, efficient WTO, ambitious China, investigation on Chinese companies, and US technology control (see Table 3).
Table 3. Construction of discourse entities in 2018 Agenda.
These entities collectively construct a context model that positions the U.S. as a protagonist addressing perceived trade challenges posed by China. The narrative crafted by the discourse producer places the reader in a mental space shaped by two dominant backdrops: the “unfair” WTO and the escalating Sino-U.S. trade conflict. In this context model, the primary participants are the United States, China, and the WTO. The setting foregrounded by the discourse producer emphasizes China’s alleged “unfair” trade practices over the past decade and portrays the U.S. as courageously countering these actions. By juxtaposing China’s supposed exploitation of the global trading system with the U.S.’s proactive responses, the Agenda establishes a moral dichotomy: a ‘just’ U.S. battling against an ‘unfair’ China, with the WTO’s role being downplayed or criticized as inadequate. This framing not only constructs the ideological landscape for the Agenda but also reinforces a narrative that seeks to align public perception with the Administration’s trade policies.
The China-related Context-deictic Space Model of the 2018 Trade Policy Agenda (hereinafter referred to as the 2018 model, see Figure 5) positions three primary participants: the United States (the ‘Self’ group), China (the ‘Other’ group), and the WTO (the ‘Other’ group). In this model, the WTO is relegated to the periphery of the Setting zone. Through repeated use of negative linguistic structures, such as “unable to do” in situation (9), the Agenda portrays the WTO as ineffective. By situating an “efficient WTO” at the distal end of the a-axis in the Setting zone—representing an unreal position—the discourse producer effectively undermines the positive image of the WTO as a fair and just international organization. This backgrounding minimizes the reader’s perception of the WTO’s role and credibility.
Figure 5. Context-deictic space model of China-related 2018 agenda.
In Figure 5, the direct opposition between China and the United States manifests across both the Setting zone (“efforts of US” vs. “China’s unfair practice”) and the Event zone (“Ambitious China” vs. “US technology control”; “Ambitious China” vs. “Investigation into Chinese companies”). Within the Setting zone, historical instances like the “US producers’ fight” in situation (10) frequently emerge to reinforce the narrative of American resistance. In contrast, the Event zone foregrounds situations such as “Made in China 2025” in situation (14), which is framed as evidence of China’s intent to “steal” American intellectual property. Similarly, situation (17) emphasizes the loss of U.S. rights, with phrases like “deprive US companies of…” placing U.S. interests in an unreal position on the a-axis, while directly highlighting contradictions with China. Collectively, these abstractions construct the United States as a vulnerable ‘Self,’ depicted as being deprived of its rightful standing by China’s actions.
China’s position in the 2018 model is encapsulated in two key discourse entities: “China’s unfair trade practice” in the Setting zone and “Ambitious China” in the Event zone. These entities portray China negatively, distorting it into the image of a non-market economy that unfairly benefits from a global trading system depicted as weakened and ineffective. Furthermore, repeated constructs such as “China is moving away from market principles” (situations (15) and (16)) reinforce this distortion in the mental space of the reader. This narrative amplifies China’s image as a deliberate violator of market norms and a manipulator of global markets.
The analysis of the 2018 model reveals several critical points. First, the WTO’s role is marginalized through a deliberate backgrounding strategy, minimizing its relevance and reliability in the reader’s perception. Second, the Agenda emphasizes the direct trade conflict between China and the United States, anchoring this conflict prominently within the Setting and Event zones of the Deictic Space. This positioning creates a palpable sense of threat and tension for the reader, influencing their spatial cognition. Under this framework, the United States is constructed as a virtuous ‘Self’ fighting to uphold justice and protect American interests, while China is depicted as an adversarial ‘Other’ engaging in unfair trade practices.
From a socio-cognitive perspective, the 2018 Agenda reflects the Trump Administration’s intention to justify its aggressive trade policies toward China by framing them as necessary resistance against a predatory economic actor. This justification is further reinforced by portraying China’s trade practices as non-compliant with global trading rules, despite their adherence to international norms. Such framing suggests an underlying aim to shape public opinion and legitimize restrictive trade measures under the guise of safeguarding U.S. interests.
5.3 Context-deictic space model of China-related 2019 agenda
The 2019 Trade Policy Agenda places significant emphasis on Sino-U.S. relations and China’s trade practices. The following discourse entities or events are manually identified in the Agenda (see Table 4): (un)accountable WTO, global trading system, China’s non-market economy, China’s unfair trade practices, China’s control of the global economy, U.S. control of the global economy, and U.S. policies. A critical backdrop to the 2019 Agenda is the Trump Administration’s portrayal of China’s trade activities over the preceding 3 years as unfair and disruptive to the global market.
Table 4. Construction of discourse entities in 2019 Agenda.
Within the context model constructed by the discourse producer, the primary participants are China, the United States, and the WTO. The model’s ‘setting’ highlights an (un)accountable WTO, a ‘negative’ image of China, and an ‘inefficient’ global trading system, framing these elements to justify U.S. trade policy measures.
In the China-related Context-deictic Space Model of the 2019 Agenda (see Figure 6; hereinafter referred to as the 2019 model), the ‘Participants’—comprising the United States (the ‘Self’ group), China (the ‘Other’ group), and the WTO (also categorized as the ‘Other’ group)—are mapped into the Deictic Space. Within the Setting zone, two key participants stand out: the WTO and China.
Figure 6. Context-deictic space model of China-related 2019 agenda.
The WTO is portrayed as an “unaccountable” and “irresponsible” institution, with the discourse producer using repeated negative expressions such as “unaccountable” and “not well-equipped” (situations 19 and 20, see Table 4). Similarly, the global trading system, closely tied to the WTO, is dismissed as flawed, as seen in situation 21, which refers to it as a “significant flawed trading system.” Through the use of past tense and negative language, an “accountable WTO” and a functioning “global trading system” are relegated to the marginal areas of the Setting zone—specifically, the distal ends of the negative a-axis (−a), s-axis (−s), and t-axis (−t). As discussed in Chapter Three, the Setting zone has a reduced impact on the reader’s cognition compared to the Event zone. This positioning effectively marginalizes the positive image of the WTO and the benefits of the global trading system, encouraging the reader to overlook or dismiss their importance.
Additionally, the Setting zone reveals a notable absence of scenarios where the United States is depicted as actively addressing so-called “unfair” trade practices. Instead, most situational models focus on constructing a negative portrayal of China as a “thief.” For example, situations 22, 23, and 24 emphasize China’s “unfair and market-distorting trading practices,” its “use of unfair practices to hurt” others, and its “non-market-oriented policies.” These narratives foreground “China’s Non-market Economy” as the dominant feature of the Setting zone, corresponding to the distal end of the s-axis, the proximal end of the a-axis, and the negative t-axis in the Context-deictic Space Model. By presenting this threatening image, the discourse producer implies that China poses a substantial risk to U.S. national security and economic interests.
In the Event zone, two direct oppositional relationships between China and the United States emerge prominently. The first and most prominent is the opposition between “China’s unfair practices” and “U.S. policies,” situated at the most proximal location to the deictic center. Most situation models describing “China’s unfair practices” are framed using active voice, with “China” as the primary subject. Additionally, these models frequently use present or future tense, as in situations 25 and 26, which emphasize imminent or ongoing “unfair trading practices in China.” Consequently, the discourse event “China’s unfair practices” is positioned at the distal end of the s-axis, aligning it with the ‘Other’ group. However, it is also placed at the proximal ends of the a-axis and t-axis, presenting these practices as immediate and realistic threats. To counteract these “unfair practices,” the Trump Administration formulates policies and provisions such as the USMCA, as reflected in situations 28 and 29. The prominence of situational models focused on U.S. policies in the 2019 Agenda underscores their importance. Accordingly, the discourse event “U.S. policies” is manually identified and located within the Event zone as part of the ‘Self’ group.
The second oppositional relationship in the Event zone involves “China’s control of the global economy” versus “U.S. control of the global economy.” Here, multiple situation models construct a threatening image of China, as exemplified by situation 27, which states, “the future of the global economy belonged to the state-driven economy of China.” This places the discourse event “China’s control of the global economy” at the distal end of the t-axis, the proximal end of the a-axis, and the distal end of the s-axis. By contrast, the discourse event “U.S. control of the global economy” is positioned as an increasingly unrealistic aspiration. Using negative expressions and future tense, such as in situation 27, where the text implies that “the future of the global economy [will not belong to] the market-based system of the United States,” the narrative suggests that China is the disruptor of U.S. economic dominance. This positioning amplifies the sense of threat by reducing the spatial distance between China (the ‘Other’ group) and the deictic center, further manipulating the reader’s spatial cognition to perceive China as an immediate adversary.
The 2019 model frames China through three dominant discourse entities: “China’s Non-market Economy” in the Setting zone, and “China’s unfair trade practices” and “China’s control of the global economy” in the Event zone. The narratives distort China’s image, portraying it as a rule-breaking nation poised to control the global economy at the expense of the United States. By reducing the spatial distance between China-related discourse entities and the deictic center, the Trump Administration amplifies the perceived threat to U.S. interests, further fueling public hostility toward China.
In the 2019 model, “U.S. policies” represent the ‘Self’ group, positioned proximally to the deictic center. These policies are framed as responses to China’s trade practices, with an emphasis on their necessity for addressing imminent challenges. Consequently, two key discourse events, “China’s unfair practices” and “U.S. policies,” form a direct oppositional relationship in the Event zone (see Figure 6). A second oppositional relationship emerges between “China’s control of the global economy” and “U.S. control of the global economy.”
Through multiple situational models, the 2019 Agenda constructs a threatening image of China, as in situation (27), which claims that “the state-driven economy of China” will dominate the global economy in the future. This positions the discourse event “China’s control of the global economy” at the distal ends of the t-axis and s-axis but at the proximal end of the a-axis, signaling an imminent threat. Conversely, “U.S. control of the global economy” is portrayed as an increasingly unattainable aspiration, framed with negative expressions and future tense to suggest that this ideal is undermined by China’s rise. For example, in situation (27), the text states, “the future of the global economy [will belong to]… not the market-based system of the United States.” This framing places “U.S. control of the global economy” at the distal end of the a-axis and far from the deictic center, while “China’s control of the global economy” is positioned closer to the center, heightening the perceived threat to the U.S.
China’s shifting position in the 2019 model is represented through three key discourse entities: “China’s non-market economy” in the Setting zone, and “China’s unfair trade practices” and “China’s control of the global economy” in the Event zone. Together, these narratives portray China as a rule-breaking, opportunistic nation that not only undermines global trade norms but also seeks to displace the U.S. as the dominant global economic power. By reducing the spatial distance between China and the deictic center, the Trump Administration intensifies the perceived threat to U.S. national interests and public welfare.
The 2019 model also marginalizes the WTO and the global trading system. Through backgrounding techniques, the discourse producer diminishes the relevance of these entities in the reader’s mental space, negating their positive contributions. In contrast, “China’s non-market economy” is foregrounded in the Setting zone, emphasizing its alleged harm to U.S. interests and setting the stage for justifying policies such as the USMCA in the Event zone. Within the Event zone, the conflict between “China’s unfair practices” and “U.S. policies” takes a prominent position near the deictic center, creating a sense of urgency and tension in the reader’s spatial cognition. The second oppositional relationship—between “China’s control of the global economy” and “U.S. control of the global economy”—while positioned more distantly, continues to amplify the narrative of China as an impending threat.
Notably, the Trump Administration’s “Make America Great Again” agenda, which seeks to restore U.S. dominance in global trade, is framed as hindered by China’s rise. This discourse shifts blame for administrative shortcomings onto China, casting it as a scapegoat. The U.S. is depicted as a victim, struggling to maintain its global influence while China is portrayed as a predatory force seizing U.S. interests and undermining the global economic order. In reality, China’s trade practices align with international norms, and no evidence supports claims of a deliberate attempt to dominate the global economy.
The 2019 Agenda reflects the Trump Administration’s intent to provoke a sense of crisis and threat among the public. By exaggerating conflict and distorting China’s image, the discourse fosters a divisive “us vs. them” narrative. As illustrated in Figure 6, the spatial relations among discourse entities vividly depict the constructed geopolitical tensions, highlighting the strategic use of discourse to manipulate public perception and reinforce hostility toward China.
6 Discussion
This study set out to investigate how China was discursively positioned in U.S. trade policy agendas from 2017 to 2019 and to introduce a new analytical tool—Context-Deictic Space Model (CDSM)—capable of systematically capturing such ideological positioning. The findings reveal a marked shift in China’s representation over the 3 years: from a relatively backgrounded economic competitor in 2017 to a foregrounded and adversarial actor by 2019. This trajectory closely corresponds to the timeline of the escalating U.S.-China trade war, particularly after 2018, when economic disagreements crystallized into formalized tariff conflicts. As the Agendas increasingly foreground China as the proximate source of “unfair trade practices,” the discourse simultaneously marginalizes multilateral institutions like the WTO, portraying them as ineffective or irrelevant.
The CDSM proposed in this study proves useful in explicating this dynamic, especially by visualizing how the U.S. Administration narrows the cognitive distance between “China” and the deictic center across time, thereby amplifying perceived immediacy and severity of the “China threat.” This manipulation of spatial and epistemic proximity in discourse contributes to constructing a sense of national urgency, justifying more confrontational trade strategies. Unlike conventional CDA approaches, which often describe such shifts qualitatively, the CDSM offers a spatially explicit and temporally comparative framework that helps map discursive escalation over time.
Theoretically, this study contributes to the socio-cognitive approach of Critical Cognitive Linguistics (CCL) by formalizing how abstract social cognition, especially the representation of ‘Self-Other’ relations, can be rendered visible through deictic configuration. While existing CCL work has emphasized ideological framing, it often lacks a systematic means for modeling how entities are mentally positioned and repositioned across multiple texts and over time. CDSM fills this gap by integrating context model with Deictic Space Model, thus enabling researchers to not only track who is foregrounded or backgrounded, but also explain how and why such positioning occurs through the interaction of contextual, temporal, and relational cues.
In contrast to the Deictic Space Model’s recent extension into Proximization Theory, which focuses narrowly on spatiotemporal convergence of threats, the CDSM extends the analytical scope to include participant roles, institutional alignments, and ideological structures embedded in context models. This broader perspective allows for a more complex understanding of how political discourse constructs not only “threat,” but also legitimacy, blame, and responsibility. In the case of the Trump Administration, the strategic centering of China and displacement of institutional mediators serve to reinforce a bilateral antagonistic worldview, bypassing multilateral norms.
From a disciplinary perspective, this study situates itself within linguistically informed political discourse analysis, expanding the methodological toolkit available to scholars of international relations, and discourse studies. By shifting focus from media discourse to diplomatic discourse, it underscores the ideological role of official diplomatic discourse such as trade policy agenda in shaping public and institutional understandings of international relations. Furthermore, in the context of intensifying U.S.-China competition, this research provides timely insight into the discursive practices that legitimize protectionism and trade unilateralism under the guise of national interest.
In sum, the CDSM offers a theoretically grounded and empirically applicable model that enhances our understanding of how political actors construct, escalate, and maintain perceived international threats. Future research could apply this model to other policy or diplomatic discourse to explore how spatial-cognitive framing interacts with ideology in global politics.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: U.S. trade policy agendas https://ustr.gov/sites/default/files/files/reports/2017/AnnualReport/Chapter%20I%20-%20The%20President%27s%20Trade%20Policy%20Agenda.pdf; https://ustr.gov/sites/default/files/files/Press/Reports/2018/AR/2018%20Annual%20Report%20FINAL.PDF; https://ustr.gov/sites/default/files/2019_Trade_Policy_Agenda_and_2018_Annual_Report.pdf. Further inquiries can be directed to the corresponding author.
Author contributions
YH: Visualization, Formal analysis, Data curation, Methodology, Software, Conceptualization, Writing – original draft, Investigation. HL: Data curation, Supervision, Resources, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research and/or publication of this article.
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.
Publisher’s note
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Keywords: context model, deictic space theory, critical discourse analysis, social cognition, political discourse
Citation: Hu Y and Li H (2025) Reframing China in U.S. Trade policy discourse: a context-deictic space model for ideological positioning. Front. Commun. 10:1598041. doi: 10.3389/fcomm.2025.1598041
Edited by:
Gisela Redeker, University of Groningen, NetherlandsReviewed by:
Raed Al-Ramahi, The University of Jordan, JordanYu Xiang, Bucknell University, United States
Copyright © 2025 Hu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ying Hu, ZmlvbmFodTEyMzRAMTYzLmNvbQ==