Building a human rights framework for workers' compensation in the United States: Opening the debate on first principles
Bibliographic record
Abstract
BACKGROUND: This article introduces the idea of human rights to the topic of workers' compensation in the United States. It discusses what constitutes a human rights approach and explains how this approach conflicts with those policy ideas that have provided the foundation historically for workers' compensation in the United States. METHODS: Using legal and historical research, key international labor and human rights standards on employment injury benefits and influential writings in the development of the U.S. workers' compensation system are cited. RESULTS: Workers' injury and illness compensation in the United States does not conform to basic international human rights norms. CONCLUSIONS: A comprehensive review of the U.S. workers' compensation system under international human rights standards is needed. Examples of policy changes are highlighted that would begin the process of moving workers' compensation into conformity with human rights standards.
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 | 0.051 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.011 | 0.048 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.017 | 0.020 |
| 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".