Bibliographic record
Abstract
Abstract With its recognition of the combined effects of the social categories of race, class and gender intersectionality has risen to the rank of feminism's most important contribution to date. Though the first intersectional research (American and British) gave visibility to the social locus of women who self-identified as “black” or “of colour”, current research goes beyond the confines of the English-speaking world and aims increasingly to develop an intersectional instrument to deal with discrimination. This project gives rise to two kinds of debate: one related to producing intersectional information and to ways of carrying out research in this area, the other to do with the use of this information in the political struggle for equality. The current paper, which is confined to the first debate, attempts to bring out the main tension points in present theorizations of intersectionality. Its objective is twofold: to demonstrate certain limits to the explanatory power of intersectionality, and to suggest ways forward in the light of discussions already in train. In order to do so four points are tackled: intersectionality as a research paradigm, the issue of levels of analysis, the theoretical difference of opinion on the ontological status of categories of difference and the issue of widening the theoretical scope of intersectionality.
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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.076 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".