Bibliographic record
Abstract
This report uses the definition of “social rights” in “The Toronto Initiative for Economic and Social Rights,” and focuses on two of these rights which have been litigated in the United States: the right to social security, at the Federal level, and the right to education, at the state level. We note that the U.S. Constitution does not expressly recognize any of the social rights listed in the introduction to this national report, and that American courts and legal scholars are generally skeptical about protecting social rights through constitutional law. The limited exceptions to this skepticism appear in the prohibition on discrimination against the indigent with respect to the exercise of “fundamental rights” like the right to travel and the guarantee of procedural rights before welfare rights may be terminated. All fifty state constitutions recognize the right to education to varying degrees, although only some deem it a fundamental right. While some state courts consider challenges to educational schemes to be non-justiciable, and defer to the legislature, others have heard such cases, most of which are based on equal protection or educational quality rationales. We conclude, however, that the United States is likely not in total compliance with the education component of the International Covenant on Economic, Social, and Cultural Rights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".