Equity/Equality, Diversity and Inclusion, and Other EDI Phrases and EDI Policy Frameworks: A Scoping Review
Notice bibliographique
Résumé
Equity, equality, diversity, inclusion, belonging, dignity, justice, accessibility, accountability, and decolonization are individual concepts used to engage with problematic social situations of marginalized groups. Phrases that put together these concepts in different ways, such as “equity, diversity and inclusion”, “equality, diversity, and inclusion”, “diversity, equity and inclusion”, “equity, diversity, inclusion, and accessibility”, “justice, equity, diversity, and inclusion”, and “equity, diversity, inclusion, and decolonization” are increasingly used, indicating that any one of these concepts is not enough to guide policy decisions. These phrases are also used to engage with problems in the workplace. Universities are one workplace where these phrases are used to improve the research, education, and general workplace climate of marginalized students, non-academic staff, and academic staff. EDI policy frameworks such as Athena SWAN and DIMENSIONS: equity, diversity, and inclusion have been also set up with the same purpose. What EDI data are generated within the academic literature focusing on EDI in the workplace, including the higher education workplace, influence the implementation and direction of EDI policies and practices within the workplace and outside. The aim of this scoping review of academic abstracts employing SCOPUS, the 70 databases of EBSCO-HOST and Web of Sciences, was to generate data that allow for a detailed understanding of the academic inquiry into EDI. The objective of this study was to map out the engagement with EDI in the academic literature by answering seven research questions using quantitative hit count manifest coding: (1) Which EDI policy frameworks and phrases are mentioned? (2) Which workplaces are mentioned? (3) Which academic associations, societies, and journals and which universities, colleges, departments, and academic disciplines are mentioned? (4) Which medical disciplines and health professionals are mentioned? (5) Which terms, phrases, and measures of the “social” are present? (6) Which technologies, science, and technology governance terms and ethics fields are present? (7) Which EDI-linked groups are mentioned and which “ism” terms? Using a qualitative thematic analysis, we aimed to answer the following research question: (8) What are the EDI-related themes present in relation to (a) the COVID-19/pandemic, (b) technologies, (c) work/life, (d) intersectionality, (e) empowerment of whom, (f) “best practices”, (g) evaluation and assessment of EDI programs, (h) well-being, and (i) health equity. We found many gaps in the academic coverage, suggesting many opportunities for academic inquiries and a broadening of the EDI research community.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,034 | 0,101 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,027 | 0,039 |
| Études des sciences et des technologies | 0,003 | 0,005 |
| Communication savante | 0,011 | 0,014 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».