Judging as Nudging: New Governance Approaches for the Enforcement of Constitutional Social and Economic Rights
Bibliographic record
Abstract
There is little agreement among legal thinkers about whether and how courts can competently and legitimately enforce constitutional social and economic rights (SERs). The principal concern is that judicial enforcement would require courts to design and manage costly social welfare programs, tasks for which judges lack the requisite democratic mandate and institutional expertise. However, courts have been increasingly willing to enforce SERs in recent years while remaining mindful of limits on their institutional capacity to do so. This article classifies and critiques the dominant ways that such courts-primarily in Canada, the United States, and South Africa-enforce SERs. It concludes that the prevailing approaches are weakest where governments have done the least to fulfill their constitutional SER obligations. Further, this article identifies emerging approaches that require structured accountability in government efforts toward SER realization. It suggests that these approaches, understood within the context of new governance or theory, better provide for the effective, coherent, and competent enforcement of SERs in the face of government recalcitrance than do the prevailing tools. The paper concludes with a case study assessing the usefulness of experimentalist SER enforcement with reference to a possible right to health under the Canadian Constitution.
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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 | 0.039 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.095 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".