Grounded and Purposive: Sir Patrick Elias and Discrimination Law
Bibliographic record
Abstract
Abstract This article argues that in his significant contribution to discrimination law, Patrick Elias’s judgments are grounded and purposive. They are purposive because they recognise that employment legislation must generally be interpreted as protective of employees because of the power imbalance between the parties. They are also grounded. They are ‘grounded in fact’ because they are practical and realistic about working life for employers and workers. They are ‘grounded in law’ because they recognise the limits of the law and that the law—even discrimination law—cannot address all injustice, real or perceived. They are also ‘grounded in the system’ because they recognise that in the current liberal (social?) market economy, employers have legitimate interests, as do employees. By grounding his judgments in the system, Elias might be seen as conservative. However, this conservatism might be considered radical in the face of much current academic discourse. Using examples from his judgments, we illustrate both the grounded and purposive approach adopted by Elias, its theoretical underpinnings, how it has been applied to labour law more widely, and how it might be used to address some thorny issues in discrimination law in the future.
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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 | 0.058 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.080 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".