Mapping trends and demographics in research on intersectional education and pedagogy: a scoping review (2020–2024)
Bibliographic record
Abstract
Intersectionality is a key framework for understanding complex experiences of inequality and discrimination in contemporary societies and specifically in educational contexts. This scoping review examines academic discourse on intersectional pedagogy from 2020 to 2024, following the methodological framework proposed by Arksey and O’Malley. Using the SCOPUS database and the keyword search ‘intersectionality’, ‘education’, ‘teaching’, and ‘pedagogy’, 48 relevant articles were identified after filtering for irrelevant keywords and excluding irrelevant document types. In addition to identifying thematic trends and research desiderata, this review systematically categorises methodological approaches, levels of education, geographical areas, years of publication and types of documents. The results show a dominant focus on Western countries (US, UK, and Canada), with smaller contributions from Australia, Iran, the Netherlands, and Ireland. The main methodological focus was on theoretical work, with a small amount of qualitative empirical work. There was no purely quantitative research. Most of the articles address issues of intersectionality and its theoretical foundations in educational contexts and in the training of educational staff, as well as didactic issues. This review highlights both progress and areas for further exploration, and points the way for future research to broaden the contextual, geographical, and methodological scope of intersectional education and pedagogy.
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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 | 0.028 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.048 | 0.060 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".