Interpreting Discrimination Law Creatively: Statutory Discrimination Law in the UK, Canada and Australia
Bibliographic record
Abstract
In the build-up to the Equality Act being passed by the UK Parliament in 2010, several calls were made for it to include a purpose clause which would guide the courts and tribunals in their interpretation of the Act’s provisions. These calls came initially from the ‘Cambridge’ Review,1 reporting in 2000 on the findings of an independent review of the enforcement of UK anti-discrimination legislation, and again in a joint statement in 2007 by the then statutory equality bodies (the Disability Rights Commission, the Equal Opportunities Commission and the Commission for Racial Equality).2 The response by the Government of the day, however, was unequivocal: a purpose clause would not be included in the proposals it put forward for an Equality Bill.3 Resolute, the Government refused to revisit its decision on this issue despite receiving further representations in favour of a purpose clause in the form of consultation responses to the Bill.4 Citing its ‘priority’ as being ‘legislation that is as clear as possible’ and dismissing the prospect of a purpose clause helping to meet this aim, the Government sought to justify its response in part through reference to its intention to consult on a Bill of Rights and Responsibilities, which would include a constitutional equality guarantee.5 Such a Bill of Rights has not, of course, come to pass.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".