Involving Humans, or Doing Good Work With Good People: Insights for Qualitative Research in Black Studies Post-2020
Bibliographic record
Abstract
." We examine the unprecedented post-2020 climate of invitation and recognition of Black scholars, Black research, "Black excellence," and Black Studies in the Canadian academy and inquire into its implications for research methodologies. We identify the current Canadian academic climate, like those that Wynter examines in her work, as emerging in the aftermath of Black death, anti-Black terror, and race rebellion. We argue that despite the ostensible epiphanies that this moment might be taken to represent, the anti-Black ordering of bodies and knowledge that Wynter outlines might well persist in the Canadian academy embedded in methodologies that produce Black people as non-human. We take seriously the possibility that the new discourses of recognition, invitation, excellence, and incorporation might be the new strategies by which BlackLife is cast beyond the realm of the Human in Canadian universities. As Wynter proffered for Black Studies, we argue that Black research cannot leave the university or its methods intact as it enters the university. We reflect on ways forward for Black researchers that insist on Black humanity in a university context that routinely denies it.
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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 | 0.135 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.038 | 0.070 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".