Methods for estimating the labour force insured by the Ontario Workplace Safety and Insurance Board: 1990-2000.
Bibliographic record
Abstract
This paper presents a methodology for estimating the size and composition of the Ontario labour force eligible for coverage under the Ontario Workplace Safety & Insurance Act (WSIA). Using customized tabulations from Statistics Canada's Labour Force Survey (LFS), we made adjustments for self-employment, unemployment, part-time employment and employment in specific industrial sectors excluded from insurance coverage under the WSIA. Each adjustment to the LFS reduced the estimates of the insured labour force relative to the total Ontario labour force. These estimates were then developed for major occupational and industrial groups stratified by gender. Additional estimates created to test assumptions used in the methodology produced similar results. The methods described in this paper advance those previously used to estimate the insured labour force, providing researchers with a useful tool to describe trends in the rate of injury across differing occupational, industrial and gender groups in Ontario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".