Inferential Construction of Disparagement in Selected Memes: A Geeian Analysis
- Maryam Abdul Kadhim Mahdi Duraye , Babylon University, English Department, College of Education for Human Sciences, Iraq.
- Prof. Nesaem Mehdi Al-Aadili (Ph.D.) , Babylon University, English Department, College of Education for Human Sciences, Iraq.
Article Information:
Abstract:
This study investigates the inferential construction of disparagement in selected memes through a Geeian analytical framework. Although memes are often approached as multimodal texts in which meaning emerges from the interaction of verbal and visual resources, this study focuses specifically on how disparaging meaning is inferred rather than directly stated. The analysis draws on James Paul Gee’s Toolkit, particularly the Fill-in Tool and the Big “D” Discourse Tool, in order to explain how meme producers rely on shared background knowledge, ideological assumptions, and socially recognizable discourses to construct derogatory meanings. Using a small set of selected English memes, the study shows that disparagement is frequently not carried by explicit insult alone, but emerges when viewers are invited to fill in unstated meanings and to recognize broader discursive identities, value systems, and social relations. The findings suggest that disparagement in meme discourse is often inferentially produced through the interaction between textual cues and culturally available interpretive frames. The study contributes to meme scholarship by clarifying how Gee’s tools can account for the discursive and inferential mechanisms through which disparagement is constructed in digitally circulating texts.
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1.INTRODUCTION:
Meme discourse is often characterized by brevity, compression, and interpretive density. In many cases, the meanings conveyed by memes are not fully stated at the linguistic surface, but depend on inference, shared assumptions, and broader discursive frames. This is particularly important in the case of disparagement, where negative meanings may be constructed indirectly rather than through explicit insult. Accordingly, the analysis of disparagement in memes requires an approach that can account for the unstated meanings that viewers are expected to reconstruct.
Gee’s discourse analytical toolkit provides a suitable framework for this task. In particular, the Fill-in Tool helps explain how meme interpretation depends on background knowledge and unstated assumptions, while the Big “D” Discourse Tool makes it possible to examine how verbal and visual cues participate in broader socially recognizable discourses, identities, and value systems. This study applies these tools to selected memes in order to investigate how disparagement is inferentially constructed and discursively stabilized. The study argues that, in the selected memes, disparagement is not primarily dependent on humor, but on inferential and discursive processes through which negative evaluation is produced indirectly.
Accordingly, based on the aims of the present study, this research seeks to answer the following questions:
1.How is disparagement inferentially constructed in selected memes through Gee’s Fill-in Tool and Big “D” Discourse Tool?
2.What discursive assumptions and socially recognizable meanings contribute to the production of disparagement in the selected memes?
2. Theoretical Framework
2.1 Disparagement
Disparagement in Mariam webster (n.d.) is defined as “to speak slightingly about (someone or something): to belittle the importance or value of (someone or something) or to lower (someone or something) in rank or reputation.”
Rossolova (2020, p. 1409) defines disparagement as a form of verbal hostility. The defamation is intended to undermine the adversary's reputation, resulting in a detrimental emotional impact. The framework of a subversive informative act suggests the presence of an imminent threat of defamation; motives directing communications designed to disparage an individual; and the process of conveying messages, encompassing strategies and techniques for expressing harm. Disparagement can be conveyed by several available resources, with the choice depending on the participants of the public protest, their objectives, and the characteristics of discourse behavior. If the recipient intends to express his negative attitude indirectly, he employs subtle ways and strategies, creating a negative context for interpreting the information in a statement.
2.1.1 Disparagement Across Contexts
Disparagement is manifested through derogatory treatment, unfavorable stereotyping, or amusement at others' expense—takes varied forms and produces different outcomes depending on the context. Research indicates that its effects are shaped by social structures, group identities, and institutional contexts, contributing to inequities in education, health, justice, and interpersonal relationships.
A. Social and Structural Contexts
Social hierarchies such as race, class, caste, and gender have disparagement profoundly rooted within them. These frameworks are responsible for the continuous differences in wealth, opportunities, and treatment that exist between people in different cultures (Darity & Ruiz, 2024). Jackson and Vander (2016) argue that the disciplines of stratification economics and intersectionality frameworks demonstrate that the origins and persistence of disparities that are related to disparagement are frequently discovered at the points where several marginalized identities intersect. They also show that policy interventions must target these overlapping systems in order to be successful.
B. Educational Contexts
Disparagement in education may be the result of dissimilarities between the cultural backgrounds of students and the cultures of educational institutions. For instance, kids from working-class backgrounds may experience feelings of devaluation when they are in academic environments that place a high importance on competition and independence. This can result in self-threat and unfair treatment from teachers (Michelmore, & Rich, 2023). In addition, there is a substantial correlation between long-term exposure to contextual disadvantage in families, schools, and neighborhoods and racial and socioeconomic gaps in educational outcomes. Gender and social identity stereotypes significantly influence students' self-concept and academic performance, with classroom attitudes and peer dynamics being crucial factors (Andersen & Smith, 2022).
C. Health and Healthcare Contexts
In the field of healthcare, disparagement manifests itself in the form of inequalities in access, quality, and outcomes that are determined by a patient's race, ethnicity, mental health state, or socioeconomic background. As a consequence of systematic biases, biases exhibited by providers, and structural restrictions, these inequities persist, which leads to underrepresented populations experiencing worse health outcomes (Conn, 2025).
D. Interpersonal and Relational Contexts
The perception of disparagement at the interpersonal level varies based on the status and relationship of individuals who are participating. Jokes that are told by people who are of a higher social status or who are members of out-groups are seen to be more hurtful and less acceptable, particularly when those jokes are directed at groups who are marginalized. Depending on the context, which includes cultural norms and power relations, disparagement can be perceived and experienced in different ways (Weitz & Koç, 2022).
2.2 Gee’s Toolkit Model (2014)
Gee’s Theoretical Tools Model (2014) is one of the most successful analytical frameworks for deciphering discourse's meaning-making processes. This model provides a series of "tools” for investigating how language functions socially and ideologically. Gee's toolset is especially useful in meme analysis since it allows to investigate how textual and environmental elements contribute to the implicit or explicit creation of disparagement. Memes frequently use complex meanings, cultural assumptions, and intertextual connections to mock, denigrate, or criticize individuals or organizations. This makes the Toolkit an effective instrument for examining not only the content of discourse but also its broader interpretation within wider social and ideological contexts.
While Gee's Toolkit (2014) has 27 tools for discourse analysis from, this study only involves two of them: The Big ‘‘D’’ Discourse Tool, and Fill in Tool. These tools are the most important for this study of disparagement in memes. The researcher chooses these tools because they offer a lot of ways to look at how meaning, identity, and cultural assumptions are made and made fun of in both text and pictures.
1. Fill-In Tool
The Fill-In Tool is one of the tools in Gee's Discourse Analysis toolkit that looks at things that aren't mentioned but are presumed to be understood by readers or listeners. It makes the analyst ask: “What needs to be filled in here to make the communication clear and meaningful?”
This tool makes it clear that language is rarely complete on its own; it often needs shared knowledge, cultural assumptions, and contextual understandings to be properly understood. Of the 27 tools introduced by Gee (2011), the Fill-In Tool is especially pertinent to this study because disparagement in memes frequently depends on implicit meanings and common cultural assumptions. In contrast to straightforward declarations, memes function through subtext and necessitate that listeners infer the unarticulated elements. This tool enables the revelation of concealed layers of sarcasm, irony, or mockery that may not be explicitly articulated in the verbal text.
Gee (2011) states:
For any communication, ask: Based on what was said and the context in which it was said, what needs to be filled in here to achieve clarity? What is not being said overtly, but is still assumed to be known or inferable? What knowledge, assumptions, and inferences do listeners have to bring to bear in order for this communication to be clear and understandable and received in the way the speaker intended it? (p.11).
2 The Big ‘‘D’’ Discourse Tool
Gee (2014) differentiates between Small “d” Discourse, denoting language-in-use or cohesive segments of language (such as conversations and written texts), and Big “D” Discourse, which includes the overarching social practices, identities, and ideologies that language and other semiotic resources facilitate and acknowledge.
Small “d” discourse denotes the distinctive spoken or written language utilized in particular settings. It pertains to the text itself, regardless of its form, such as a dialogue, a speech, a blog entry, etc. Big “D” Discourse, on the other hand, transcends mere language, encompassing the manners in which individuals behave, engage, and portray themselves within the world. It encompasses a synthesis of words, activities, values, beliefs, and social positions that collaboratively construct a discernible identity or activity.
In the context of meme disparagement, the Big "D" Discourse Tool is critical for detecting how the meme relies on and supports prevailing discourses—such as racism, sexism, or nationalism—to normalize satire or ridicule. It allows to examine not only what is said, but also how it draws on larger social narratives to generate its humor and power.
Gee (2014) says:
For any communication, ask how the person is using language, as well as ways of acting, interacting, believing, valuing, dressing, and using various objects, tools, and technologies in certain sorts of environments to enact a specific socially recognizable identity and engage in one or more socially recognizable activities. Even if all you have for data is language, ask what Discourse is this language part of, that is, what kind of person (what identity) is this speaker or writer seeking to enact or be recognized as. What sorts of actions, interactions, values, beliefs, and objects, tools, technologies, and environments are associated with this sort of language within a particular Discourse? (p.181).
3. Data Analysis
Meme 1
Idolatry through the ages (Derek, 2025)
1. Fill-in Tool
The meme’s meaning depends on what the viewer must supply to make the “then/now” equivalence work. The text does not explain why the contemporary scene should count as “idolatry”; it simply labels it “Idolatry now,” requiring the viewer to accept an unstated premise that venerating the Marian statue constitutes worship in the same sense as worshipping the golden calf. A further inference is that the moral judgment attached to the historical case transfers directly to the contemporary one. The viewer must also fill in the idea of functional sameness: because both scenes show a crowd oriented toward a central object, the meme presumes that the social action is identical across time, even though historical, doctrinal, and contextual differences are not addressed.
2. Big ‘‘D’’ Discourse Tool
At the level of Big “D” Discourse, the meme enacts a world in which religious practices are divided into clear moral categories: “true worship” versus “idolatry.” Within this world, the category “idolatry” is treated as stable and cross-historical, so labeling something “now” as idolatry is sufficient to delegitimize it. The meme also constructs a boundary between an implied in-group that recognizes and rejects idolatry and an out-group whose practice is positioned as religiously illegitimate. The “then/now” framing presents continuity as common sense, encouraging the audience to view the contemporary practice through a historically condemned template. This enacted discourse world makes the comparative move socially legible: the meme invites alignment with the stance that the present practice should be rejected on the same moral grounds as the past one.
Meme 2:
Islam Is a Religion of Peace (Buchoff, 2024)
1. Fill-in Tool
The meme’s message is not fully stated; it relies on the viewer to complete several assumptions. First, the viewer must supply a link between the verbal claim “Islam is religion of peace” and the visual scene, treating the image as “evidence” that challenges the claim. Second, the viewer must infer that the Muslim-coded figure represents Islam as a collective category, not an individual case. Third, the viewer must infer a causal relation: the labeled skull groups are interpreted as victims attributed to Islam, even though no explicit sentence states “Islam killed them.” Fourth, the viewer must accept that the contradiction between “peace” and the violent symbolism should be read as exposure or refutation, not as unrelated juxtaposition. These inferences are necessary for the meme to make sense as a coherent argument rather than two disconnected elements.
2. Big ‘‘D’’ Discourse Tool
At the level of Big-D discourse, the meme enacts a polarized world in which religious identities are evaluated through moral outcomes. It constructs an in-group position aligned with skepticism toward Islam’s “peace” claim and an out-group position assigned to Islam and its followers as a threatening identity. The list of multiple out-group labels (atheist, Buddhist, Hindu, Jews, Christians) expands the social stakes and frames Islam as harmful across communities, not in a single conflict. In this discourse world, the peace label is treated as propaganda or hypocrisy, and the visual field is treated as the “real truth.” The meme therefore authorizes distrust and moral exclusion as reasonable stances.
Disparagement becomes stabilized here because Gee’s tools show how the meme guides interpretation beyond what it explicitly says. The Fill-in requirements force the reader to complete a refutation narrative (peace claim vs violent “reality”), while Big-D discourse positions Islam as an illegitimate or dangerous identity category in the viewer’s social map. In other words, the meme’s disparaging effect is not only a visual insult or a verbal claim; it is a completed inferential stance that becomes socially legible through the discourse world it constructs.
Data Discussion and Results:
The analysis of Meme 1 and Meme 2 shows that disparagement is constructed inferentially rather than fully expressed at the textual surface. In both memes, Gee’s Fill-in Tool reveals that the viewer must supply unstated assumptions in order to complete the derogatory meaning. In Meme 1, the meme depends on the inference that the contemporary religious practice shown is equivalent to the historically condemned case of idolatry. In Meme 2, the viewer must infer that the Muslim-coded figure stands for Islam collectively and that the violent imagery functions as evidence against the claim that Islam is a religion of peace. In both cases, the disparaging force relies on interpretive completion rather than explicit insult.
The Big “D” Discourse Tool further shows that these inferences are stabilized within broader discourse worlds. Meme 1 constructs a discourse in which religious practices are morally classified as either legitimate worship or idolatry, while Meme 2 constructs a discourse of Islam as a threatening and exclusionary identity. The results indicate that disparagement emerges through the interaction of two processes: inferential completion and discursive framing. The Fill-in Tool explains how the unstated insult is recovered, and the Big “D” Discourse Tool explains how that insult becomes socially and ideologically meaningful.
Conclusion:
This study has shown that Gee’s Fill-in Tool and Big “D” Discourse Tool provide an effective framework for analyzing the inferential construction of disparagement in meme discourse. The analysis of the two memes demonstrates that disparagement is not always explicitly stated, but often depends on what the viewer is expected to infer and on the broader discourse world within which that inference becomes meaningful. In Meme 1, disparagement is constructed through the transfer of historical religious condemnation to a contemporary practice, while in Meme 2 it is produced through the refutation of a peace claim by means of violent visual symbolism and discursive generalization.
Overall, the findings confirm that disparagement in memes can be explained as a twofold process: first, the viewer completes the unstated derogatory meaning; second, that meaning is stabilized through socially recognizable discourses of moral judgment, exclusion, and identity. In this sense, Gee’s tools are especially useful for showing how memes produce disparagement indirectly yet effectively, making inferential and discursive mechanisms central to the interpretation of derogatory meaning in digital texts.
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Web sources
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10. Derek, (2025). Why Protestants get Marian devotion wrong. The Bold Catholic. https://theboldcatholic.substack.com/p/why-protestants-get-marian-devotion