Bibliographic record
Abstract
Throughout this paper, I use a political and activist lens to think about disability arts and its potential role in opening up a necessary conversation around how madness is produced by experiences of racism, poverty, sexism, and inter-generational trauma within the Black community. I begin by explaining how the Black body has a history of being the site of medical experimentation. From the perspective of my own experience, I suggest that this history of medical abuse has caused Black people to be suspicious and wary of the healthcare system, including the mental healthcare system, which forecloses discussions around the intersection of Blackness and mental health. I go on to argue that this discussion is further silenced through the trope of the ‘strong Black woman,’ which, in my experience works to perpetuate the idea that Black women must bear the effects of systemic racism by being ‘strong,’ rather than society addressing this racism, and she must not admit the toll that this ‘resilience’ might have on her mental health. I close with a discussion of how my art practice seeks to open up a conversation about madness in the Black community by suggesting that madness is political.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.120 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".