Mentoring is not an Excuse for Bringing in One Negro at a Time (Creative Intervention)
Bibliographic record
Abstract
This poem was originally a cultural object to be included for analysis in a research article that appears in this journal issue, co-authored by Chasia Jeffries, Mariel Rowland, and myself."Pulling Ourselves Together: Looking Towards Black Reparative Theory and Pedagogy in Post-George Floyd Higher Education" (Jeffries et al., 2024) explores the groundworks (a decade plus of collective Black feminist campus activities and capacity building) that undergirded Chasia and Mariel joining the graduate program.As members of the Inaugural (and now defunded and defunct) Black Studies Graduate Cluster in the School of Humanities they have a particularly important role to play in analyzing the material conditions that make their knowledge production possible.We wrote that research article during their preparation for the master's degree examination milestone based on their concerns about the discontinuation of the Black Studies Graduate Cluster.It was important to me to bequeath a perspectival and embodied history about what activities had created their belonging; had sustained the demand for their presence; and had welcomed and longed for their research agendas.As co-authors, we decided to analyze only a small portion of the poem in the research article and to publish the entirety of it as a standalone creative intervention.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".