Non-Standard Employment Contracts, P. 1 Differences in Benefits Within Non-Standard Employment Contracts
Why this work is in the frame
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Bibliographic record
Abstract
his review of the conference paper, Felice Martinello and the Institutional Reviewer from Statistics Canada for their review of an earlier draft, and we would like to acknowledge Caroline Weber’s contributions in the earlier versions of this paper. The article represents the views of the authors and does not reflect the opinions of Statistics Canada. For correspondence, contact zeytino@mcmaster.ca or gcooke@mun.caNon-Standard Employment Contracts, P. 2 Differences in Benefits Within Non-Standard Employment Contracts This paper examines benefits coverage among regular full-time, regular part-time, temporary full-time, and temporary part-time workers and asks whether there are significant differences in benefits coverage within non-standard workers, i.e. those in the latter three categories. Statistics Canada’s Workplace and Employee Survey (1999) data is used for the analysis. Results suggest that regular full-time workers are more likely to receive benefits than all others, followed by those in regular part-time and temporary full-time employment contracts. As expected, temporary part-time workers have the lowest benefit coverage, suggesting that they are the most marginalized within non-standard employment. Non-Standard Employment Contracts, P. 3
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it