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Record W3036566451 · doi:10.24908/pceea.vi0.14170

WHAT WOMEN STUDENTS WANT THEIR INSTITUTIONS TO DO TO MAKE ENGINEERING EDUCATION MORE INCLUSIVE AND LESS “CHILLY”

2020· article· en· W3036566451 on OpenAlexaffvenueabout
Cori Hanson

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEngineering educationAcknowledgementInclusion (mineral)Diversity (politics)Equity (law)Work (physics)Sexual orientationHigher educationUnderrepresented MinorityPublic relationsSociologyEngineeringPolitical sciencePedagogyMedical educationGender studiesEngineering managementComputer scienceMedicine

Abstract

fetched live from OpenAlex

It has been over 30 years since science and engineering classrooms were first described as “chilly” environments for women. Since then many engineering programs in Ontario have worked to diversify their student populations with a particular focus on recruiting more women into engineering education. Despite the increase in the number of women enrolled in engineering education, incidents of sexism and microaggressions based on sexual orientation and race continue to be experienced by women in these programs. In many engineering faculties in Ontario work on equity, diversity and inclusion is still fairly new. Members of the community who are marginalized need to be consulted in order for this work to be impactful on their experience. This paper uses an intersectional framework to present preliminary results on what women studying in undergraduate engineering programs believe their universities could do to make engineering education more inclusive. This paper argues that women students in engineering are still in search of a community. Our women students want more representation of women in engineering, further education and awareness of equity issues, acknowledgement of their experiences and opportunities to connect with other women in engineering.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2020
Admission routes3
Has abstractyes

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