Can (or should) white women do Black feminist theory?: exploring tensions, contradictions, and intersectionalities while performing justice-focused research
Bibliographic record
Abstract
Black feminist thought is produced by and for Black women, but could it be applied by – be the conceptual lens for – others? How do/should I position myself as a white woman doing Black feminist work? How do I de-centre myself, centre Black voices, and also use the power that my whiteness provides to do the type of social justice work Black feminist thought approaches demands? Here, I wrestle with the answers to these questions mainly through my ‘conversations’ with Black feminist intellectuals such as Collins (2000), Lorde (2007), Cooper (2018), and Kendall (2020). In this reflective piece, I explore six ‘lessons learned’ which emerged from the tensions/conflicts I encountered while doing this project in the confines of academia with the goal of considering how academics and practitioners can create knowledge in anti-racist ways.
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 | 0.037 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.061 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| 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".