Bibliographic record
Abstract
Abstract This chapter considers ways of making equality more effective by improving methods of enforcement and remedies. Section II assesses the individual complaints model, demonstrating its limitations as an engine for change. Reliance on a complaints model places an inordinate burden on individual victims and has little impact on systemic and structural discrimination. Section III examines ways in which the complaints model might be strengthened, through class actions, strategic litigation, and agency enforcement, and touches on how courts can be mobilized by social movements. Section IV turns to approaches which depart from the dependency on individual initiative and instead require proactive action from those in the best position to bring about change. It assesses several approaches, such as the public sector equality duty in the UK, Canadian employment equity legislation, and contract compliance in the US. Despite the great potential of proactive measures, in practice they easily slide into mere bureaucratic compliance. This section briefly attempts to explain these challenges in terms of different regulatory models, such as reflexive law, responsive law, and endogenous theories of law. The challenge remains to achieve the appropriate synthesis between harnessing the energy of responsible bodies to provide creative responses to systemic discrimination, and effective regulatory measures which can pierce the façade of compliance. Central to this is the need to involve those who are affected, as well as trade unions and civil society stakeholders, in defining the problem and in holding bodies to account.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".