Bibliographic record
Abstract
In this commentary, I discuss two important moves in Katharine Jenkins’s Ontology and Oppression: the development of a pluralist conceptual framework for social kinds and the rejection of an ontology-first approach to social kinds in emancipatory social struggles. While Jenkins discusses gender kinds most thoroughly, my analysis focuses on race kinds. I connect Jenkins’s use of the constraints and enablements framework (CEF) with my own argument for thinking about race as a technology. I also connect her arguments against ontology-first approaches with my own claim that we ought not to focus on the metaphysics of race and should instead try to uncover its explanatory power in different contexts. While our approaches differ—particularly with respect to their positions along an analytic (Anglo-American)–Continental spectrum—they also resonate. Picking up on Jenkins’s brief discussion of transracialism, I also use her rejection of the ontology-first approach as a framework for identifying the insidiousness of transgender–transracial comparisons.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.010 | 0.031 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".