Le climat diversité et les pratiques de gestion des ressources humaines favorisant l'EDI en milieu de travail
Bibliographic record
Abstract
RÉSUMÉ : La gestion de l'équité, la diversité et l'inclusion (EDI) constitue un enjeu pour les gestionnaires et pour les employeurs. Sa mise en œuvre demeure complexe et pose des défis pour les organisations. Dans ce contexte, la gestion de l'EDI mérite d'être étudiée, car elle implique un engagement réel des employeurs pour créer un environnement de travail inclusif. Ce mémoire se concentre sur le climat diversité et sur les pratiques de gestion de l'EDI dans les milieux de travail. L'objectif principal est de dégager les pratiques de gestion de l'EDI qui sont déployées dans les milieux de travail. L'étude vise à examiner ces pratiques selon le profil des organisations. Pour cela, une recherche quantitative a été menée auprès de 149 employeurs dans des organisations de différents profils. Un questionnaire en ligne a été diffusé afin de recueillir des données sur leurs pratiques en matière d'EDI. Plus précisément, l'étude s'est intéressée au climat diversité, au diagnostic EDI, aux pratiques en matière de gestion de l'équité, de la diversité et de l'inclusion ainsi qu'au profil des organisations. Les résultats révèlent que la plus forte dimension du climat diversité dans les organisations est celle de la praxis, c'est-à-dire que les comportements et les attitudes des membres des organisations à l'étude sont favorables à la diversité. Cependant, peu d'organisations s'intéressent au diagnostic EDI. En revanche, les grandes organisations et celles du secteur parapublic semblent les plus actives à cet égard en évaluant la composition de la main-d'œuvre ainsi que certaines activités de ressources humaines (RH), notamment le recrutement. Les résultats indiquent aussi que les objectifs qui guident les initiatives EDI les plus visées concernent l'amélioration du climat d'inclusion, l'élargissement du bassin de recrutement et la diversification de la main-d'œuvre. Enfin, il les activités les plus souvent déployées au regard de l'EDI par les organisations à l'étude touchent le recrutement, la présélection et la sélection du personnel, l'accueil et l'intégration du personnel, ainsi que la formation et le développement des compétences. -- Mot(s) clé(s) en français : Équité, diversité, inclusion, climat diversité, diagnostic EDI, inclusion, Canada. -- ABSTRACT : Managing equity, diversity, and inclusion (EDI) is a challenge for managers and employers. Its implementation remains complex and poses challenges for organizations. In this context, EDI management deserves to be studied because it involves a real commitment on the part of employers to create an inclusive work environment. This thesis focuses on the diversity climate and EDI management practices in workplaces. The main objective is to examine management practices that promote equity, diversity, and inclusion in workplaces. The study aims to examine these practices according to the profile of the organizations. To this end, quantitative research was conducted among 149 employers in organizations with different profiles. An online questionnaire was distributed to collect data on their EDI practices. More specifically, the study focused on the diversity climate, EDI diagnosis, equity, diversity, and inclusion management practices, and organizational profiles. The results reveal that the strongest dimension of the diversity climate in organizations is that of praxis, meaning that the behaviors and attitudes of the members of the organizations studied are favorable to diversity. However, few organizations are interested in EDI diagnostics. On the other hand, large organizations and those in the parapublic sector seem to be the most active in this regard, evaluating workforce composition and certain human resources (HR) activities, particularly recruitment. The results also indicate that the objectives guiding the most targeted EDI initiatives relate to improving the climate of inclusion, expanding the recruitment pool, and diversifying the workforce. Finally, the activities most often deployed with regard to EDI by the organizations studied relate to recruitment, pre-selection and selection of personnel, onboarding and integration of personnel, as well as training and skills development. -- Mot(s) clé(s) en anglais : Equity, diversity, inclusion, diversity climate, DEI assessment, inclusion, Canada.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".