Bibliographic record
Abstract
[This paper is part of the Focused Collection on Instructional labs: Improving traditions and new directions.] This review article provides an overview of research on the topic of gender equity in educational physics labs. As many institutions and instructors seek to evolve or transform physics lab learning, it is important that changes are made that improve equity for all students along multiple axes of identity, including gender. The studies highlighted in this review article describe the existence of complex gender-based differences, e.g., in opportunities to tinker with lab equipment, as well as differences in grades, conceptual understanding, and motivational outcomes across a broad range of lab curricula and contexts. The studies also illustrate and explore social interactions and structures that can impact students’ experiences based on their gender identities. Although there has been less scholarship focused on proposals to reduce gender-based inequities in labs, this review article also provides an overview of some relevant proposals as well as associated research results. This overview of research on gender equity in physics labs helps to make clear that future scholarship on equity in physics labs should adopt gender frameworks that allow researchers to transcend binary gender identities and student deficit framing of research results. Likewise, a case is made that future research is needed on equity along other axes of identity, as well as research that accounts for the intersectionality of different identities, in the physics lab context. Published by the American Physical Society 2024
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".