Reconsidering 'Whiteness as Property': Bad Property for Personhood and Affirmative Action Rhetoric
Bibliographic record
Abstract
Reconsidering “Whiteness as Property”: Bad Property for Personhood and Affirmative Action Rhetoric Remarks offered by Margaret Jane Radin at the 2014 UCLA annual Critical Race Studies Symposium. The Symposium, entitled Cheryl Harris, Whiteness as Property: A 20-Year Appraisal, honored Harris on the 20th anniversary of publication of her ground-breaking article laying out the case for considering whiteness as property. This talk revisits Radin's theories of property and personhood and of rhetorical capture in the context of revisiting Harris's article.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.054 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| 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".