“Why Me?”– Questions of Racial Equity and Institutional Racism in Academia
Bibliographic record
Abstract
Beginning with the necessary question “Why me?,” I look at a system which bars BIPOC bodies and theory. In her open letter to the US Black Studies academic community, Sylvia Wynter (1994 ) spoke about the problem of “no human involved” (“NHI”) in the policing and incarceration of Black bodies as being pertinent for how Black studies was positioned institutionally. This same white supremacist governance and surveillance “NHI” exists in universities on both sides of the Atlantic. There is something very wrong with the system of which I am a part that persistently and consistently bars BIPOC bodies and theory and only avails our presence and thought a marginal position on the proviso that the status quo of whiteliness ( Yancy 2008 ) is not disturbed. Nothing really changes in terms of anti-BIPOC racism. Rather, it remains strangely the white supremacist (settler) colonial same within Canadian race-evasive multiculturalism and UK ‘post-race’ racism.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.096 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".