Influence des microagressions sur le bien-être en milieu de travail
Bibliographic record
Abstract
RÉSUMÉ : En raison de la couleur de leur peau, les personnes s’identifiant comme minorités visibles sont confrontées au phénomène de microagression, notamment en milieu de travail. Ce type d’agression n’est pas sans répercussions pour les personnes qui en sont victimes. Ce mémoire porte donc sur l’influence de la microagression sur le bien-être des personnes en milieu de travail. L’objectif général de ce travail de recherche est de comprendre les effets potentiels des microagressions sur le bien-être des personnes en milieu de travail. Au moyen d’entrevues semi-dirigées avec des personnes en emploi qui s’identifient comme minorités visibles, la présente étude emprunte une démarche qualitative. Plus précisément, cette étude a exploré les expériences de microagressions vécues par des travailleurs noirs en milieu de travail, au Québec. Nos résultats montrent que les expériences de microagression nuisent au bien-être des travailleurs. Des pistes de solutions et d’actions sont suggérées pour les organisations soucieuses de créer une culture d’inclusion et que les employeurs adoptent une posture plus ouverte, pacifique et empathique face à la diversité humaine. -- Mot(s) clé(s) en français : gestion de la diversité, microagressions, milieu de travail, minorités visibles, culture organisationnelle. -- ABSTRACT : Due to the color of their skin, people who identify as visible minorities face the phenomenon of microaggression, particularly in the workplace. This type of attack is not without repercussions for the people who are its victims. This dissertation therefore focuses on the influence of microaggression on the well-being of people in the workplace. The general objective of this research is to understand the potential effects of microaggressions on the well-being of people in the workplace. Using semi-structured interviews with employed people who identify as visible minorities, this study takes a qualitative approach. This study explored the experiences of microaggressions experienced by Black workers in the workplace in Quebec. Our results show that microaggression experiences harm workers’ well-being. Possible solutions and actions are suggested for organizations keen to create a culture of inclusion and for employers to adopt a more open, peaceful and empathetic posture in the face of human diversity. -- Mot(s) clé(s) en anglais : diversity management, microaggressions, workplace, minorities visible, organizational culture.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".