Choosing between simple and complex remedies in socio-economic rights cases
Bibliographic record
Abstract
Recent work by Kent Roach argues that the best way for judges to protect significant violations of rights is through a two-track approach that combines simple and complex, dialogical remedies. He insightfully shows that simple and complex remedies perform different roles and vindicate different conceptions of justice. This article considers the role of simple and complex remedies in enforcing socio-economic rights. The author argues that the ideal remedy for widespread socio-economic rights violations will only sometimes, but not always, run on two tracks. Under common conditions, courts will want to work on only one of the two tracks, by issuing either a simple remedy or a complex remedy. This article analyses the factors that should guide courts in choosing between simple and complex remedies in socio-economic rights cases.
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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 | 0.059 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.014 | 0.042 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".