Resource Questions in Social and Economic Rights Enforcement: A Preliminary View
Bibliographic record
Abstract
A growing number of constitutions guarantee judicially enforceable social and economic rights (SER). This book chapter examines whether courts, when asked by claimants to give positive effect to such rights, consider and take into account limitations on national resources and constraints on the government’s budget. The chapter surveys 32 leading SER cases from eight jurisdictions: Argentina; Canada; Colombia; India; Venezuela; Germany; the United States; and South Africa. This pilot study reveals that judicial thinking and behavior with respect to resource limitations on the fulfillment of SER is remarkably diverse, not only between but even within jurisdictions. The principal finding is that courts often pay strikingly little attention to concerns that agitate academics and jurists wedded to traditional theories of separation-of-powers and judicial deference. There is evidence of emerging consensus on two points. First, most if not all courts studied hold that financial constraints on government do not justify infringement of constitutional rights (including SER) barring a situation that can be identified as a serious economic emergency. Second, most if not all courts studied hold that while legislative and executive budgetary allocations and fiscal trade-offs are entitled to respect, beyond a certain point such a trade-off can so badly disserve as to infringe a constitutionally guaranteed right. Accordingly, prudential trade-offs made by the political branches in the course of giving effect to SER are in principle subject to judicial review.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".