Equality of Treatment, Opportunity, and Outcomes: Mapping the Law
Bibliographic record
Abstract
Abstract Equality is a concept open to many interpretations in the legal domain, with equality as equal treatment dominating the scene in the bureaucratic nation-state. But there are many possibilities offered by legal instruments to go beyond strict equality of treatment, in order to ensure equality of opportunity (a somehow nebulous concept) and equality of outcomes. Legislation can be sorted along a continuum, from the most discriminatory ones (“negative discrimination laws”) such as laws that prescribe prison sentences for people accused of being in same-sex relationships, to the most protective ones, labeled as “mandated outcome laws” (i.e., laws that prescribe quotas for designated groups) through “legal vacuum” (when laws neither discriminate nor protect), “restricted equal treatment” (when data collection by employers to monitor progress is forbidden or restricted), “equal treatment” (treating everyone the same with no consideration for outcomes), “encouraged progress” (when data collection to monitor progress on specific outcomes is mandatory for employers), and mandated progress (when goals have to be fixed and reached within a defined time frame on specified outcomes). Specific countries’ national legislation testify that some countries moved gradually along the continuum by introducing laws of increasing mandate, while (a few) others introduced outcome mandates directly and early on, as part of their core legal foundations. The public sector tends to be more protective than the private sector. A major hurdle in most countries is the enforcement of equality laws, mostly relying on individuals initiating litigation.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 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".