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Record W2933306516

Focusing Efforts to Improve Gender Equity in Physics Education: A Pan-Canadian Physics Education Research Network

2018· article· en· W2933306516 on OpenAlexaffabout
Lindsay Mainhood

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsQueen's University
Fundersnot available
KeywordsExcellenceEquity (law)Physics educationGender equityPolitical sciencePublic relationsEngineering ethicsPhysicsSocial scienceSociologyEngineeringPedagogy
DOInot available

Abstract

fetched live from OpenAlex

Women participating in physics play an essential role in Canada’s economic and research future through their potential to maximize research excellence and broaden horizons with diverse perspectives. However, the underrepresentation of women in physics remains a pressing concern for Canada, especially in higher education since the culture of inequity in the field impedes both its own and women’s progress. Research suggests a need for further physics education research and networking to increase the number of women in physics; the degree of collaborations between researchers in physics education is less than in other science fields due to fragmented connections. Currently, there is limited knowledge of (a) the status of physics education research (PER) across Canada (especially relating to gender), (b) Canadian universities’ stance on PER and its mobilization, and (c) how researchers, policy makers, and practitioners are working together in a network to improve the underrepresentation of women in physics. This proposed study aims to fill these knowledge gaps and investigate what capacity a pan-Canadian physics education research network may have to develop a culture of gender equity in the field.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.143
GPT teacher head0.400
Teacher spread0.257 · 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 designQualitative
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

Citations0
Published2018
Admission routes2
Has abstractyes

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