Regulation as Intervention: How Regulatory Design Can Affect Practices and Behaviours in the Workplace
Bibliographic record
Abstract
This chapter explores the ways in which regulatory measures affect workplace practices that contribute to or impede the development of workplace bullying. It begins with an examination of the concept of regulation, looking at regulation by the state, as well as by non-state actors that perform a regulatory function. In the second part, to illustrate the concept of regulation as an intervention, we will draw on experiences from the implementation of the legal regime in Québec to show how a regulatory regime can lead to the mobilization of different categories of actors to perform different types of activities in and outside the workplace. The third part of this chapter provides a synthesis of the issues that need to be discussed in the choice of regulatory instruments, to ensure that the legal environment promotes practices that are known to have a positive impact on the workplace and concretely contribute to the reduction of the causes of workplace bullying and harassment.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".