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Record W3047526281 · doi:10.25071/1916-4467.40470

Gender Equity in Physics Education: Modeling a Future for Canadian Physics Education Research

2020· article· en· W3047526281 on OpenAlexaffvenueabout
Lindsay Mainhood

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsQueen's University
Fundersnot available
KeywordsEquity (law)Delphi methodPhysics educationDelphiGender equityPresentation (obstetrics)Political scienceMathematics educationSocial scienceSociologyPsychologyComputer scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

The purpose of this early-stage study is to determine if and how Canada’s physics education researchers are working to solve the problem of women’s underrepresentation in physics education, and to develop an expert opinion-based model for institutions to address gender equity issues in physics education (at all levels). The study will: 1) identify physics education practices that physics education research (PER) experts have found to be supportive of gender equity; 2) identify Canada’s PER experts and their research focuses; (3) conduct a Delphi study with Canada’s PER experts to gain consensus on how PER can inform and support gender equity in physics education; and (4) develop a model to guide ongoing PER in Canada to support the achievement of gender equity in physics education. Results of preliminary phases of the study include emergent themes from interviews with international PER experts on gender-equitable physics education practices and initial descriptions of the landscape of PER in Canada. These are based on content analysis of online biographies for all individuals working in Physics or Education departments across all Canadian universities. The presentation aims to generate discussion on these results and the proceeding phases of the study.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.206
GPT teacher head0.426
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes3
Has abstractyes

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