Pulling Ourselves Together: Embracing Black Feminist Reparative Theory and Pedagogy in “Post-George Floyd” Higher Education
Bibliographic record
Abstract
This article considers how institutions of higher education participated in the national “racial reckoning” that followed the murder of George Floyd in May 2020. Using the work of Pan-Africanist jurist Motsoko Pheko, memoirist Sisonke Msimang, poet Audre Lorde, and Black queer feminist critics Tiffany Willoughby-Herard and M. Jacqui Alexander, the authors reflect on the principled research practices and ethos that catalyze sustainable repair. Durable forms of repair include reconnecting the feeling body with the knowing self, stillness, and tarrying. The authors (two doctoral students and a professor colleague) also consider repair through attention to the material conditions of knowledge production (collaborative writing, reclaiming the sacred, and questioning what it means to make something whole without reproducing a singular dominating episteme) to disrupt academic hierarchies. Arguing that repairing society, the planet, or the ways that questions are asked and answered requires ongoing wrestling with our current climate of racial terror in higher education, this article embodies the authors’ reparative principles and envisions paths towards educational justice.
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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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.033 | 0.077 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".