From Laggard to Leader: Canadian Lessons on a Role for U.S. States in Making and Implementing Human Rights Treaties
Bibliographic record
Abstract
Human rights treaty-making and implementation pose special\nchallenges for federal states. The unique quality of human rightsinherent,\nuniversal, urgent, and compelling-and the existence of\nentrenched domestic rights-protecting instruments give rise to\ncomplexities that distinguish these treaties from their international\ncounterparts. Of particular and problematic significance for federal states is\nthe fact that human rights treaties "made" by the national government\noften implicate the relationship between the individual and the sub-unit\ngovernment, requiring substantive compliance at the local level. In Canada\nand the United States, the distinctive nature of human rights has colored\nthe process of treaty-making and implementation, posing delicate legal,\npolitical, and practical questions about the division of powers in these\nfederal states. In response to these challenges, Canada has worked to\nresolve the apparent tension between its federal structure and international\nhuman rights law, while the United States
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.019 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.023 | 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".