Bibliographic record
Abstract
This piece discusses the online and offline discourses on the lives and bodies of Black femme and nonbinary individuals and the harm that is so casually inflicted upon us. Through popular stories of harm performed around famous Black women, such as with rapper Megan Thee Stallion, I connect the history of Black women in popular culture to current online spaces that continue to minimize and trivialize our trauma. I seek to highlight that these stories are not an anomaly, but rather sentiments rooted in the misogynoir that is so entrenched in western culture and have been expanded and weaponized within the online sphere. In addition, the piece challenges the universality of the Black Lives Matter (BLM) movement in its implementation, criticizing its propensity to forget its feminine victims. It is important to emphasize where it has failed and where it needs to be intentional about the people it has overlooked, as this is a movement that began online, where this harm is currently taking place, and at the hands and energies of Black femmes, the very people getting hurt. This piece has manifested from many conversations already occurring in online Black feminist spaces about our treatment and our needs. It invites others into the fold and seeks to encourage individuals to critically reflect on how Black femme and non-binary individuals are presented on their timeline in-between the numerous BLM posts that claim to protect them.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.028 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".