An Introduction to Anti-Black Sanism
Bibliographic record
Abstract
Sanism is an oppression. It makes normal the practice of discrimination, rejection, silencing, exclusion, low expectations, incarceration, and other forms of violence against people who are othered through mental ‘illness’ diagnosis, history, or even suspicion. Of particular concern for us are the sanist experiences of racialized people who identify as Black, African, or of African descent, for we and others have long noted and experienced an anti-Black crisis in mental health diagnosis and “care.” For instance, young Black men are diagnosed with schizophrenia more than any other group, Black children are being psychiatrized at higher rates, and in our experience on the front lines here in Toronto, more Black-identified patients are being held against their will in hospitals. Is what we are seeing here a kind of sanism, a particular form of racism, or something combined that has not yet been named? In 2013, we three authors began to call this place of intersection anti-Black Sanism, starting a historical, theoretical, methodological, personal, and practice conversations in our community work, in our research, and in our classrooms. In this article, we outline our analyses thus far. We also chart the responses we have had to date, responses of the community, research, and pedagogical kinds. We detail how the anti-Black Sanist experience makes itself present in multiple places and spaces complicating “care,” critique, and madness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".