Bibliographic record
Abstract
Aubrey Graham, more commonly known as hip-hop performer Drake, presents himself as a man of contradiction—a lover and a fighter, sensitive but hard, successful but humble. Despite this subjective work, designed to present a complex embodiment of an artistic and financial success, the discourse of Graham online is often underpinned by suspicion and derision that seeks to redefine him as a pretender who is unworthy of the status he claims. Nowhere is this more evident than in the “Wheelchair Drake” memetic cluster, which uses an old Degrassi: The Next Generation promotional image of Graham sitting on a wheelchair, combined with humorous juxtaposition of rap lyrics, to critique Graham’s status as both a performer and a Black man. In various Wheelchair Drake memes, physical impairment becomes a living metaphor for a spoiled identity; the memes argue that, just like ableist imaginations of physically disabled people, Graham is doomed to a life of impotence and dependency. Built upon a sample of 583 user-generated images, coded into 9 thematic groups, this article excavates the latticed discourses of masculinity, disability and race that animate the Wheelchair Drake meme and consider the ways that this memetic cluster subjects Aubrey Graham to the strictures of ableist hegemonic masculinity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".