Equity/Equality, Diversity and Inclusion, and Other EDI Phrases and EDI Policy Frameworks: A Scoping Review
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.027 | 0.039 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".