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Record W4413341458 · doi:10.5204/mcj.3183

They Not like Us

2025· article· en· W4413341458 on OpenAlexaboutno aff
Guilherme Giolo, Daniel Trottier, Simone Driessen

Bibliographic record

VenueM/C Journal · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Introduction: From Rap Battles to Cultural Battlegrounds In 2024, rappers Kendrick Lamar and Drake took music, popular culture, and even politics by storm with an escalating series of diss tracks (Coscarelli), the latest chapter in a long-simmering rivalry dating back to 2014. Feuds have long defined rap, with diss tracks forming a distinct sub-style (Baker). The most infamous precedent comes from the 1990s, when American rappers from the East Coast and West Coast clashed (lyrically, symbolically, even physically) over artistic authenticity, territorial claims, and personal vendettas. The Kendrick–Drake feud, however, was not just another iteration of this tradition. Whereas earlier rap conflicts revolved around lyrical combat and street credibility, this feud unfolded into a sprawling cultural event, transcending music. Fans debating the feud online transformed it into a pop culture vortex, touching on everything from the Trump–Harris race (Dyson) to cancel culture and racial identity (as Drake is Canadian, born to a white mother and an African American father—Williams). This feud became a proxy for cultural affiliations, political positions, and aesthetic sensibilities, making where one stood in the feud symbolic of one’s stance on larger issues, all while avoiding a more prominent polarised mapping in matters of ‘woke’/’based’ or red-state/blue-state seen in other instances of popular culture and corporate branding. This dynamic points to a broader shift in meaning-making within digital culture, extending beyond rap battles. Increasingly, cultural meaning is not just explicitly stated through symbols but implied through interconnected references and assemblages. Consider the ‘brat green’ phenomenon: when British singer Charli XCX used a vivid green on an album cover, the colour gained an unexpected second life, incorporated into activist protests, Kamala Harris’s campaign aesthetics, and broader expressions of youthful irreverence (Holtermann). Similarly, emerging ‘Internet aesthetics’ (such as ‘cottagecore,’ or ‘dark academia’) combine disparate artistic styles, musical genres, and lifestyle elements into overarching labels that convey meaning through tacit association (Giolo and Berghman). We refer to this process as cultural aggregation: a mode of meaning-making that operates by layering (particularly pop) cultural references into loosely coherent symbolic webs, rather than through singular symbols and semantic clustering. Cultural aggregation is increasingly visible in Internet culture, ranging from mood boards to aestheticised trends like ‘Pastel QAnon’, which blends wellness and mommy influencer visuals with conspiracy theories (Argentino 87). Meaning-making through cultural aggregation remains under-theorised beyond specific case studies. Cultural aggregation speaks to changes that can be observed in fan studies as well as in digital media practices more generally. User-generated content, blurred production and consumption, are taken for granted in fandoms and digital fora (Jenkins, “Fandom”) Yet, the continued saturation of content (including commentary) creation, combined with the prevalence of hashtags (Williams), memes (Zienkiewicz), and platform architecture/affordances on sites like Reddit, generate conditions where content is both locally meaningful but also central to default scrolling practices on platforms like Instagram and TikTok. These conditions erode articulation, with meaning less often stated directly and, more frequently, tacitly assembled through loosely connected assemblages of references. Consider meme templates that combine highly recognisable rhetoric with obscure pop-culture references and localised content that may be indecipherable beyond small audiences. As connections are drawn between visual, memetic, and other elements aggregated from rap feuds, political struggles, and conspiracy theories (to name a few), interpretations are ambiguous and sometimes contradictory, yet remain meaningful. Taking the Kendrick-Drake feud as a high-profile case study, this article bridges concepts from media studies (Philips and Millner; Jenkins, Convergence Culture) like hashtag, remix cultures, and Internet ambiguity; and fan studies (Driessen, Jones, and Litherland; Jenkins, Textual Poachers) including fan readings and textual poaching, to explore how cultural aggregation operates and shapes conversations within digital media spaces. Using content analysis, we highlight how Reddit users engaged with the feud across three categories of communities: specialised rap fora (e.g., r/HipHopHeads), artist-specific fandoms (e.g., r/KendrickLamar), and broader pop culture discussion spaces (e.g., r/PopCultureChat). Through this, we explore how cultural aggregation functions as a framework for understanding recurring patterns of engagement with music, fandom, and digital discourse. Diverse audiences engage with densely encoded and contextually bound cultural references. In doing so, they mobilise differing types of content knowledge to assert multiple understandings of the feud. These findings bear resemblance to existing literature on Web 2.0 practices such as hashtag culture (Sheldon, Herzfeldt, and Rauschnabel) or remix culture (Markham), but also suggest that cultural aggregation operates as an evolving meaning-making mechanism distinct from these earlier forms. Rather than simply repurposing symbols, cultural aggregation incorporates them into networks of association, shaping cultural, social, and political discourse in online spaces. In doing so, it speaks to the need for “theoretical tools that both respond to and constitute communication in new ways, with new ways of conceiving its object of analysis” (Slack 143). However, this research also underscores the need for further investigation into cultural aggregation’s mechanisms, boundaries, and broader applications across digital and offline contexts, particularly concerning the spreadability of digital content. Understanding how meaning is increasingly shaped through assemblages that invoke affect and tacit understandings, rather than discrete symbols, presents an important avenue for future research in media studies, communication, and cultural theory. Analytical Considerations Seeking to uncover more generalised dimensions of aggregation and how fans make sense of both this rap feud through broader cultural phenomena, as well as cultural phenomena through the feud, we conducted an in-depth qualitative analysis of conversation threads related to the feud occurring across Reddit communities. While there are online fandoms beyond Reddit, this platform cultivates organically emergent discourse within pseudonymous, interest-based communities, enabling researchers to observe meaning-making practices in situ (Bury). We adopted an exploratory approach to meaning-making in fan communities (e.g., r/KendrickLamar), general hip-hop communities (e.g., r/HipHopHeads), and more generally across Reddit. We employed the platform’s keyword search both in predetermined subreddits and more generally, in order to identify aggregation practices and meaning-making. Search terms were limited to the following: artist names (“Kendrick”, “Drake”), general culture keywords (“beef”, “diss”), and interpretive signals (“discussion”, “explained”, “controversy”), employed within selected subreddits and in general Reddit search queries. We collected threads between April 2024 and April 2025, selecting those that (a) discussed or interpreted the feud’s meaning, or (b) mobilised the feud and its elements in broader conversations, and (c) showed signs of traction or resonance (e.g., through layered comment interaction, upvotes, or cross-referencing). Future work could extend this sampling logic to underrepresented or more ambivalent communities, or apply comparative strategies to threads with minimal engagement. This multi-layered search logic can be seen as a system of concentric circles. First, more devoted rap fans naturally engage with musical artefacts through predominantly lyrical and aesthetic modalities, fan communities tend to include larger crowds interested in the artists’ music but also in their popular culture personae, given that both Kendrick and Drake are immensely popular figures even outside the rap world. Finally, general communities are concerned with popular culture as a whole, of which rap music is only a part. By following this layered sample logic, we aimed to account for the breadth of this rap feud’s cultural ramifications, which (unlike previous rap battles) expanded beyond the rap community and fans. Following sampling, the resulting corpus contained 22 threads from 10 subreddits: r/KendrickLamar r/Drizzy r/hiphopheads r/Hiphopcirclejerk r/Fauxmoi r/PeterExplainsTheJoke r/OutOfTheLoop r/PopCultureChat r/professorskye r/AlternateHistory Corpus selection and interpretive coding were carried out through a combination of individual close reading and collaborative analysis. The authors met regularly to exchange notes, refine patterns, and ensure coherence in thematic observations, with contributions from a research assistant. To identify moments of aggregation, we focussed on interpretations and appropriations of the feud that gained traction, as indicated by visible uptake such as upvotes, recurring phrasing, or nested comment threads. Our analysis emphasised moments in which meaning was collectively constructed or performatively shared, even in the absence of explicit agreements. Following, we illustrate key patterns of cultural aggregation observed across the sample and how they differentiate from earlier modes of digital fan discourse. Findings: Aggregation Practices and Discourses The Kendrick vs. Drake feud transcended rap music culture (with its own logics of appreciation) to become part of broader conversations across social and cultural life. In this sense, we observed three main ways in which Reddit users aggregated different topics

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.619
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.210
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2025
Admission routes1
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