Getting at Equality: Research Methods Informed by the Lessons of Intersectionality
Bibliographic record
Abstract
This article evaluates a Participatory Action Research (PAR) approach with mixed methods including concept mapping, q-sorting and deliberative dialogue in the context of a research project on young people’s experiences with digital communications technologies, and addresses some of the central insights of intersectionality theory and praxis. Our approach seeks to ensure that, insofar as possible, the gathered data provide a rich and layered window into the experiences of young people from a range of marginalized communities served by our project partners. The article revisits some key insights and contestations relating to intersectionality and addresses their relationship to our approach. We evaluate whether these methods enhance understandings of the interactions of structures of subordination with other factors identified in intersectionality scholarship, as well as the extent to which they centre the knowledge and expertise of those subordinated by matrices of domination as discussed by authors such as Crenshaw and Hill Collins. Our approach is just one of many that social science researchers interested in advancing intersectionality’s key insights could deploy. While it falls short of full consistency with these insights, its mixed methods work toward our partners’ social justice objectives while facilitating exploration of intersecting axes of subordination. Our approach can also help our project recapture the politic at the heart of many intersectional feminist critiques, such as those of Crenshaw and Hill Collins - that reconceptualizing knowledge requires centring the knowledge and expertise of those traditionally excluded due to interlocking systems of subordination.
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 | 0.302 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.015 | 0.075 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.007 | 0.034 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".