Reflections on the State of Workers’ Compensation and Occupational Health & Safety in the United States and Canada
Bibliographic record
Abstract
When an article similar to this one was published in Canada, it reflected primarily knowledge derived from firsthand experience in several provinces and territories, and empirical research in Ontario and British Columbia. Since that article was published in 2013, this new article reflects my research on recent publications in the United States on Workers’ Compensation. The article begins by explaining the damaging and overwhelming significance of experience rating. To enhance an understanding of current situations, the article then explains the legal history of Occupational Health & Safety and Workers’ Compensation. Then the role of physicians is discussed, particularly the difficulties they often have in distinguishing questions of law from questions of medicine. The article then deals with how decisions are made in the claims department of a Workers’ Compensation Board and by appeals tribunals. The practice of actuaries is explained, including the problems that their role creates. The limited role of judicial review is then mentioned, and finally the significance of the North American Free Trade Agreement and the World Trade Organization. The article concludes with conclusions and comments.
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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.019 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".