Notice bibliographique
Résumé
Purpose The purpose of this paper is to review the literature on intersectionality and ascertain its potential for application to human resources (HR) research and practice. Particular attention is paid to its methodological issues involving how best to incorporate intersectionality into research designs, and its data issues involving the “curse of dimensionality” where there are too few observations in most datasets to deal with multiple intersecting categories. Design/methodology/approach The methodology involves reviewing the literature on intersectionality in its various dimensions: its conceptual underpinnings and meanings; its evolution as a concept; its application in various areas; its relationship to gender-based analysis plus (GBA+); its methodological issues and data requirements; its relationship to theory and qualitative as well as quantitative lines of research; and its potential applicability to research and practice in HR. Findings Intersectionality deals with how interdependent categories such as race, gender and disability intersect to affect outcomes. It is not how each of these factors has an independent or additive effect; rather, it is how they combine together in an interlocking fashion to have an interactive effect that is different from the sum of their individual effects. This gives rise to methodological and data complications that are outlined. Ways in which these complications have been dealt with in the literature are outlined, including interaction effects, separate equations for key groups, reducing data requirements, qualitative analysis and machine learning with Big Data. Research limitations/implications Intersectionality has not been dealt with in HR research or practice. In other fields, it tends to be dealt with only in a conceptual/theoretical fashion or qualitatively, likely reflecting the difficulties of applying it to quantitative research. Practical implications The wide gap between the theoretical concept of intersectionality and its practical application for purposes of prediction as well as causal analysis is outlined. Trade-offs are invariably involved in applying intersectionality to HR issues. Practical steps for dealing with those trade-offs in the quantitative analyses of HR issues are outlined. Social implications Intersectionality draws attention to the intersecting nature of multiple disadvantages or vulnerability. It highlights how they interact in a multiplicative and not simply additive fashion to affect various outcomes of individual and social importance. Originality/value To the best of the author’s knowledge, this is the first analysis of the potential applicability of the concept of intersectionality to research and practice in HR. It has obvious relevance for ascertaining intersectional categories as predictors and causal determinants of important outcomes in HR, especially given the growing availability of large personnel and digital datasets.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».