Making or Administering Law and Policy? Discretion and Judgment in Employment Standards Enforcement in Ontario
Bibliographic record
Abstract
Abstract The purpose of this paper is to advance an approach to analyzing decision-making by front line public officials. The notion of discretion in front line decision-making has been examined widely in the law and society literature. However, it has often failed to capture the different kinds and levels of decisions that enforcement officials make. Taking an interdisciplinary approach that draws on political, sociological, and legal analysis, we propose a new conceptual framework, one that draws a sharper distinction between discretion and judgment and teases out distinct levels in the scope and depth of decision-making. We then use this framework to create a conceptual map of the decision-making process of front-line officials charged with enforcing the Employment Standards Act (ESA) of Ontario, demonstrating that a deeper, more precise analysis of discretion and judgment can contribute to a richer understanding of front line decision-making and its social, political, and legal implications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".