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Record W4415070773 · doi:10.31235/osf.io/rxg86_v1

Evaluating STEM/Engineering Identity of Elementary and High School Students in STEM Outreach Programs

2025· preprint· en· W4415070773 on OpenAlexaffabout
Yas Oloumi Yazdi, Chia-Chien Selina Tai, Chenxi Jenny Ge, Xin‐Yang Liu, Jessica Wolf, Kaylin McLeod, Robyn Newell, Katherine Lyon, Agnes D’Entremont, Jenna Usprech, Karen C. Cheung

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicEducational Research and Pedagogy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOutreachIdentity (music)FeelingPerceptionSelf-conceptField (mathematics)

Abstract

fetched live from OpenAlex

The effectiveness of STEM outreach programs in fostering interest and sense of belonging can be assessed through participants’ STEM Identity (SI) and Engineering Identity (EI). SI/EI involve an internal feeling of belonging in STEM/Engineering and recognition of belonging from others. This paper assesses how attending a one-week STEM/Engineering camp at a Canadian university changes SI/EI and their subscales (Self-Recognition; Self-Efficacy; Interest; Recognition by Others) among elementary (grades 4-7) and high school (HS; grades 8-12) youth who completed quantitative surveys before and after the camp. SI was assessed for elementary and EI for HS students. There was no significant change in SI, but self-recognition and recognition by others increased significantly while interest and self-efficacy decreased. HS students showed a small but significant increase in EI, self-efficacy, and recognition by others. The results suggest that such programs maintain positive perceptions of the field but may not cause a large or immediate shift in SI/EI.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.003
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 head0.121
GPT teacher head0.459
Teacher spread0.337 · 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 designObservational
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
Published2025
Admission routes2
Has abstractyes

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