Using story-based methodologies to explore physics identities: How do moments add up to a life in physics?
Bibliographic record
Abstract
[This paper is part of the Focused Collection on Qualitative Methods in PER: A Critical Examination.] This article details methodologies employed to enable sharing and coconstructing the stories of three women’s lives in physics. The first case explores the usefulness of timeline interviewing, where participants narrate episodes that are coconstructed with the researcher as meaningful over time. We illustrate this method in the case of a mature student in Sweden from a working-class background who shared moments that added up to a life outside of physics and then a sharp turn into physics later in life. The second case explores life-history interviewing using a narrative-inquiry approach and deep relationship building which enabled the coconstruction of stories of experiences over time. These moments are coconstructed with the researcher and analyzed using an intersectionality lens to yield a story depicting the transnational experiences of a woman of color moving across various European contexts into the North American physics context. The final case is of a first-generation Canadian woman of color who shared her navigations of in and out of school physics via a method known as the “Rivers of Life.” Using this method, the participant narrates their experiences with physics as a river, using metaphorical tools like rafts, rocks, rapids, tributaries to discuss various moments described as twists and turns over time that together amount to a life in physics. We discuss the value of different approaches to coconstructing narratives with participants and, in particular, the need for this kind of research in physics contexts.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".