“Ok, gender! Where are you?!”: On the potential of catalytic validity in feminist geographies of everyday inequities
Bibliographic record
Abstract
Abstract This paper is a provocation and reflection on some of the methodological tensions and opportunities I encountered in trying to “get at” gender in my research on the everyday gendered geographies of physical activity. Situated in a feminist methodology, I designed my study on gender and gym environments to include multiple forms of data (interviews, drawings, journals) to foster a participant‐centred research process by offering diverse forms of expression. Yet, there were moments where my feminist commitment to recognise participants’ agency in how they articulated their gendered experiences was stymied by seeming self‐contradictions throughout their multi‐modal narratives, as well as by my researcher interpretations of gender. In this post‐mortem, I consider what incongruities in the ways some participants spoke about gender within and across data types tell us about gender as a concept. I illustrate how there is methodological opportunity in using multiple creative methods as a check for rigour in the form of “catalytic validity,” or awareness‐raising among research participants, which is consistent with socially transformative feminist geographical aims.
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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 | 0.135 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.128 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".