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Record W4405674819 · doi:10.24908/pceea.2024.18567

Assessing STEM Outreach for historically marginalized groups in engineering: STEM/engineering identity and role models

2024· article· en· W4405674819 on OpenAlexafffundvenue
Shouka Farrokh, Mehmooda Evelyn-Piprawala, Naomi Endale, Jessica Wolf, Kaylin McLeod, Karen C. Cheung, Agnes D’Entremont

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of British Columbia
FundersCentre for Blood Research, University of British ColumbiaSocial Sciences and Humanities Research Council of CanadaUniversity of British Columbia
KeywordsOutreachIdentity (music)Engineering ethicsEngineeringPolitical scienceLawAestheticsPhilosophy

Abstract

fetched live from OpenAlex

Evaluating outreach efforts can include measuring changes in factors such as Engineering or STEM Identity. STEM/Engineering Identity involves an internal feeling of belonging and recognition from others (e.g., family, teachers). Strong STEM/Engineering Identity is associated with entering STEM careers. This pilot study examines one large outreach program to determine: are there changes in STEM or Engineering Identity after a weeklong day-camp? Are there differences associated with gender, age, race, or intersectional identities, and/or having role models or parents in engineering? Elementary campers (grades 4-7), high school campers and volunteer Junior Instructors (both grades 8-12) were surveyed pre- and post-camp. We measured STEM Identity for elementary campers and Engineering Identity for teenagers through validated survey measures. Teenagers were also asked about role models, having parents in engineering, and career goals. While there were small increases in STEM/Engineering Identity, participants generally started with high STEM/Engineering Identity and frequently had role models 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.025
GPT teacher head0.247
Teacher spread0.222 · 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 designTheoretical or conceptual
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
Published2024
Admission routes3
Has abstractyes

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