Lonely methods and other tough places: recuperating anti-racism from white investments
Bibliographic record
Abstract
This article wrestles with how white domination is reproduced in research methods, questions and priorities in the neoliberal university. Reflecting on the stuck and lonely places in my doctoral project, I consider the challenges of doing research on racism in institutions largely hostile to such inquiries. I also trace the pivotal insights that helped me to get unstuck and less lonely. This involved refusing to allow white audiences and white investments to determine the direction and priorities of anti-racist scholarship. The academy constantly returns us to the authority of these gatekeepers and this needs to be displaced and replaced with forms of accountability that do not consolidate white authority about matters pertaining to racism. The question of how to engage responsibly with the harm of racial violence became a central one as the concerns, priorities and desires of Black and racialised women rerouted questions of audience and accountability in this research project. Instead of being faithful to academic forms and conventions, I follow the insights of Black, Indigenous and women of colour feminisms to argue for a practice of careful and ethical engagement with one another.
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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 | 0.059 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.027 | 0.085 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".