Dealing with ‘vulnerable workers’ in precarious employment: Front-line constraints and strategies in employment standards enforcement
Bibliographic record
Abstract
Individual worker complaints continue to be the core foundation of employment standards enforcement in many Western jurisdictions, including the Canadian province of Ontario. In the contemporary labour market context where segments of the labour force may be disproportionately impacted by rights violations, and employment relationships are more diverse and often more tenuous than previously, the continued reliance on individual claims suggests a need to better understand the challenges associated with the investigation and resolution of claims involving ‘vulnerable workers’ in precarious employment situations. Using interviews with front-line Ontario employment standards officers (ESOs), this article examines the extent to which certain worker characteristics and employment situations perceived by officers as ‘vulnerable’ are identified by officers as significant constraints or barriers to investigation processes and outcomes, and documents whether and how officers address these constraints and barriers. The analysis also identifies the perceived influence of policy, resource and legislative requirements in shaping how officers deal with the more difficult and challenging cases, while also considering the extent to which the officers’ actions are understood by them as discretionary and guided by their particular orientations or concerns. In so doing, this article reveals challenges to the resolution of claims in precarious employment situations, the very place where employment standards are often most needed.
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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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.045 | 0.031 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".