Applying the lens of intersectionality to UK equal opportunities and diversity policies
Bibliographic record
Abstract
Abstract Incorporating the social differentiations of gender, race, disability, sexual orientation, religion or belief, and age into UK equal opportunities and diversity legislation involved creating a new single equality body for Great Britain (England, Scotland, and Wales). This paper assesses the efficacy of an Equality and Diversity Mainstreaming approach established in Northern Ireland (NI) for this new agency. It also stresses the potential value of “intersectionality” to further policy planning and development. It is argued that the combination of the NI model underpinned by the concept of intersectionality could offer a way forward for the new agency where, importantly, the issue of gender remains one of the most important axes of domination. Copyright © 2010 ASAC. Published by John Wiley & Sons, Ltd.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.013 | 0.083 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".