Bibliographic record
Abstract
This essay explores the online response to hip-hop artist Megan Thee Stallion and the shooting incident involving her and fellow rapper Tory Lanez. In this essay, the theories of spreadable misogyny, by Suzanne Scott, and anti-fandom, by Jonathan Gray, are employed to examine the ways in which Stallion's anti-fans engaged in spreadable misogynoir towards her, particularly in the aftermath of the shooting. The essay analyzes the dynamics of online fandom, the reframing of Stallion as a culprit rather than a victim, and the erasure of her identity as a Black woman. It discusses the ways in which memes, and commentary on the situation by fans, anti-fans, and other celebrities were used to minimize the Stallion-Lanez shooting and the effect that had on Stallion, other Black female celebrities and other Black women. The essay also highlights the complex position Black women are placed in when seeking justice for themselves. Furthermore, it investigates the emergence of a faux fandom for Lanez built on demonizing Stallion. Through an examination of social media reactions, memes, and public discourse, this essay highlights the pervasive misogynoir faced by Stallion and Black women in and outside the music industry and the challenges they encounter in asserting their autonomy and seeking justice for the harm done to them by their male counterparts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".