Baby Steps? Toward the Regulation of Temporary Help Agency Employment in Canada
Bibliographic record
Abstract
This paper provides a critical examination of recent policy developments in Canada towards the regulation of temporary help agency employment in Canada. The contextual description provided includes analysis of recent trends in the growth of temporary help agency employment in Canada, and a review of emerging labor policy concerns. Policy “problems” are identified, under explicit normative assumptions provided in the paper. Subsequently, the paper then provides an assessment of the trajectory of Canadian policy reform through a review of four key policy process “moments” within three different regulatory jurisdictions in Canada (the provinces of Ontario and Quebec, as well as the Federal jurisdiction), which collectively reveal the nature of recent policy discourse and the trajectory of policy development. Chronologically, these four moments were the establishment by the Quebec provincial government of the “Bernier Commission” and the publication of the “Bernier Report” in 2003; the establishment of the Federal Labour Standards Review Commission by the federal government (the “Arthurs’ Commission”) and the publication of the “Arthurs’ Report” in 2006; the sponsoring of Ontario’s Bill 161 in 2007, a private member’s Bill that was ultimately not passed by the Ontario legislature; and the legislative passage of Bill 139 in Ontario in May 2009. It is argued that in light of the overall context, including salient policy concerns and contrasting developments in Europe, recent Canadian developments and dialogue are comparably minimal, yet reveal aspects of a pattern in Canadian policy trajectory or, the direction of “baby steps” in this domain.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| 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".