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Record W4200245123 · doi:10.1152/advan.00032.2021

Participation value of undergraduate students leading STEM outreach: evaluation of academic, personal, and professional effects

2021· article· en· W4200245123 on OpenAlexafffund
Elizabeth Saville, Jennifer M. Jakobi, Anne Beaudoin, Sabre Cherkowski

Bibliographic record

VenueAJP Advances in Physiology Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsUniversity of British Columbia, Okanagan CampusOkanagan University CollegeUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsOutreachMedical educationProfessional developmentPsychologyMedicinePolitical science

Abstract

fetched live from OpenAlex

This study explores the academic, professional, and personal career benefits of leading science, technology, engineering, and mathematics (STEM) outreach, from the perspective of undergraduate student leaders. We consider traditional and nontraditional STEM university students, gender, and type of position (paid or volunteer) in this evaluation. Data were collected through an online survey completed by 30 former student STEM outreach leaders from the University of British Columbia. Self-reported survey data indicated that STEM outreach had a moderately strong average impact of 6.89 ± 1.55 out of 10 on participants' personal, academic, and professional development, with no significant differences between these areas. Outcomes did not differ between genders, and paid work was found to contribute to greater personal and professional impact. The positive influence of outreach on academic and professional decision making was higher in traditional STEM fields than STEM-based health-science studies. The outcomes of this research adds insight into positive impacts of STEM outreach on university student instructors.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.046
GPT teacher head0.433
Teacher spread0.387 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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