Bibliographic record
Abstract
We live in a world characterized by multiple, overlapping and plural normative orders embracing formal and informal legal regimes, customs and practices. Legal protections for equality and protections against discrimination are found in a plurality of legal instruments, including international, regional, national, state and municipal human rights documents and institutional codes of conduct. Moreover, formal equality rights operate in social and cultural contexts that are deeply influenced by the customs, norms and social practices of everyday life. In assessing how law may be used to reduce group-based inequalities, therefore, it is critical to examine the interaction between different sources of formal human rights protection and diverse, overlapping and coexisting social and cultural orders – or regimes of informal law. Such an exploration provides important insights into systemic, structural and social obstacles to effective enforcement of formal anti-discrimination and equality rights protections – obstacles institutionalized and embedded in both official and unofficial law and custom. Moreover, an appreciation of the intersections and interactions between a plurality of legal orders (both formal and informal) illuminates how strategic reliance on different sources of protection may advance the effective enjoyment of the right to equality. In this paper, I highlight how the plurality of law affects equality rights in institutional, community and global contexts.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".