“They Have All Been Faithful Workers”: Injured Workers, Truth, and Workers’ Compensation in Ontario, 1970-2008
Bibliographic record
Abstract
In interviews with injured workers with serious and/or complex injuries, a clear belief emerged that for them to be successful in their compensation claims they had to persuade a regular array of decision-makers that their injuries resulted from accidents that occurred “in and or out of the course of employment,” that they feel the way they say they are feeling, that they can and cannot do what they say they can and cannot do, and that they are capable of learning what they say they are capable of learning. They must, in short, convince these decision-makers (family doctors, employers, Workers Compensation Board [WCB] doctors, and medical specialists, as well as WCB adjudicators) that their story is the truth. The author’s presence as a researcher added another layer to the discursive process, in that the author, too, became someone injured workers had to inform, and, if necessary, convince about their truths. It was in this sense that the authority for the author’s research into the struggles of injured workers became, in Michael Frisch’s terms, a “shared authority.” The author’s written words about their stories and their struggles were to be used to help the decision-makers, the researchers, and the wider public understand that faithful workers were truthful workers.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.032 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".