The Duty to Prevent Emotional Harm at Work: Arguments from Science and Law, Implications for Policy and Practice
Bibliographic record
Abstract
Although science and law employ different methods to gather and weigh evidence, their conclusions are remarkably convergent with regard to the effect that workplace stress has on the health of employees. Science, using the language of probability, affirms that certain stressors predict adverse health outcomes such as disabling anxiety and depression, cardiovascular disease, certain types of injury, and a variety of immune system disorders. Law, using the language of reasonable foreseeability, affirms that these adverse outcomes are predictable under certain conditions, typically defined in relation to what a reasonable person should know. Society is arguably in a position to establish standards for the abatement of certain types of workplace stress. As part of this process, we need to conceptualize an ideal form of conduct that exemplifies the standards to which both law and science urge us to aspire. For this purpose, the concept of the neighbor at work is proposed.
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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 | 0.086 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.143 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.056 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".