Bibliographic record
Abstract
This paper assesses the emergence of a new proactive model to achieve gender equality, and compares it with the more established complaints-led model based on individual rights. While transcending many of the weaknesses of the individual complaints model, the proactive model remains ambiguous in many crucial respects, particularly as to its objectives, its use of participation, and how compliance is to be achieved. The paper aims to shed more light on these key aspects by drawing on the experiences of such models in Canada, Northern Ireland, Britain, and the EU itself. This demonstrates that the location of proactive strategies on the borderline between law and politics makes them highly dependent on political will. The key challenge is therefore to ensure that proactive strategies are based on a recognition that equality is a fundamental right, not a discretion, without reverting to individualised complaints mechanisms with all their inbuilt weaknesses. I conclude by considering how we might achieve a fundamental and non-derogable core of rights within a proactive model.
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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 | 0.038 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.066 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".