Gender Equity in Physics Education: Framing the Future of Physics Education Research in Canada
Bibliographic record
Abstract
The overarching purpose of this dissertation is to determine if and how the field of physics education research (PER) in Canada is addressing issues of gender inequity in physics education, and to develop an expert knowledge-based framework to guide Canada’s ongoing PER to increase gender equity in physics education. Three separate but related research phases were designed to achieve this purpose and address specific research questions: What can be learned from PER experts about supporting gender equity in physics education? What is the landscape of PER in Canada; who are our PER experts and what are their areas of research focus? What do Canada’s experts consider to be the most pressing needs and priorities to address the gender equity issue? And what is a framework that could guide and support Canada’s ongoing PER to achieve gender equity in physics education? The research questions were approached with a pragmatic worldview, a feminist and critical theoretical lens, and a qualitative-dominant mixed-methods paradigm, and addressed using qualitative and mixed-methods methodologies—content analysis, interviews, questionnaires, and the Delphi technique. Phase one describes the sought-after lessons from experts in gender equity PER in the form of three major themes relating to what is currently known and unknown about gender equity in physics education, moving toward gender-equitable physics education, and conducting and using PER as a tool. Phase two describes, for the first time, the landscape of PER in Canada in terms of characteristics of Canadian PER engagers and their areas of research focus, as well as five themes representing this complex field with challenges to its members and progress. Phase three describes resulting consensus levels from the Delphi process concerning what Canada’s physics education researchers consider to be priorities for addressing gender inequity in physics education, as well as six themes representing the commentary during this structured communication process. The data from all three phases of this research are synthesized and integrated into an iterative framework intended to guide, both practically and theoretically, ongoing PER in Canada to increase gender equity in physics education.
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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 | 0.042 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.056 | 0.042 |
| Scholarly communication | 0.033 | 0.011 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".