Integrating intersectionality into occupation-based research: Reflections on methodological challenges and potential
Bibliographic record
Abstract
Abstract Since its inception in the 1970s, intersectionality has been taken up across various disciplines, drawn on as a theory, framework, guiding lens, critical tool and beyond. Despite origins in Black feminism and original applications aimed at articulating the process of marginalization of Black women, intersectionality has since been acknowledged for its utility in diverse contexts. However, within occupational science and occupational therapy academic contexts specifically, intersectionality remains largely discussed in a theoretical sense with insufficient critical application of an intersectional research approach that situates diverse social markers of difference and occupations within systems of power. The purpose of this paper is to contribute to closing this gap, highlighting the challenges and potential of intersectional occupation-based research. In addition to outlining four main postulations for applying (or ‘actioning’) intersectionality in occupation-based research generated by the authors through engagement with theoretical and research texts, examples illustrating their own attempts to integrate an intersectional approach into our research are shared. Critical reflexivity on these examples points to alignments with an intersectionality approach and the understandings these enabled, as well as challenges and limitations in our applications. The potential and future directions of intersectionality within occupation-based research and practice are then discussed.
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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 | 0.376 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.037 | 0.171 |
| Scholarly communication | 0.046 | 0.049 |
| Open science | 0.010 | 0.054 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 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".