Why the solutions put in place to combat moral harassment in the world of work so far failed to eradicate the phenomenon? Comparative analysis between France and Canada.
Bibliographic record
Abstract
This thesis will focus on psychological harassment in the workplace. As we are studying management and international issues, we will analyse moral harassment in France and Canada. In the first part, we will analyse psychological harassment in its global form. We will study its sources, its forms and its sustainability. \nWe will also analyse the consequences this has on victims, companies and relatives. Then we will study the actions put in place to fight against moral harassment. In the course of this paper, we will see that moral harassment remains a taboo subject in companies, which allows the phenomenon to continue. \nThis research highlighted the need to continue to measure the phenomenon and evaluate the control actions put in place to keep only those that could prove effective. The studies, surveys and memoirs that we hope to see in the future will be, each at its own level, useful in order not to forget this scourge.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".