Bibliographic record
Abstract
Abstract This article considers the use of comparison in establishing multi-ground claims of intersectional discrimination. Leading examples of test cases from the US and the UK exemplify the challenges in using comparison to establish discrimination against Black women, based on the grounds of both race and sex. These challenges include: the insistence on using a single mirror comparator (viz white men) or the difficulties in choosing multiple comparators from a range of options (viz white women, Asian women, Black men, white men etc); the missing rationale for the selection; and the unwieldiness in actually appreciating the nature of intersectional discrimination based on this exercise. To overcome these, Canadian courts have relaxed the strict requirement of necessarily resorting to comparison for proving discrimination and switched to the flexible approach. However, in practice, flexible approach appears as fastidious as strict comparison in its selection and use of comparators. Thus, neither of the two approaches has been too helpful in supporting intersectional claims. The article argues that instead, a useful way of proving intersectional discrimination is to follow the South African approach of making comparisons contextually: (i) between all relevant comparators, identified in reference to one, some, and all of the grounds or personal characteristics; and (ii) sifting through comparative evidence with the purpose of establishing similar and different patterns of group disadvantage which characterise the nature of intersectional discrimination. This approach brings both principle and purpose to employing comparison and can be especially useful in appreciating intersectional discrimination as based on multiple grounds.
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 | 0.031 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.057 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".