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Record W4386843364 · doi:10.1111/cdev.14007

Girls are good at STEM: Opening minds and providing evidence reduces boys' stereotyping of girls' STEM ability

2023· article· en· W4386843364 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueChild Development · 2023
Typearticle
Languageen
FieldPsychology
TopicScience Education and Perceptions
Canadian institutionsSimon Fraser UniversityUniversity of British ColumbiaYork UniversityUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyIntervention (counseling)Developmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Girls and women face persistent negative stereotyping within STEM (science, technology, engineering, mathematics). This field intervention was designed to improve boys' perceptions of girls' STEM ability. Boys (N = 667; mostly White and East Asian) aged 9-15 years in Canadian STEM summer camps (2017-2019) had an intervention or control conversation with trained camp staff. The intervention was a multi-stage persuasive appeal: a values affirmation, an illustration of girls' ability in STEM, a personalized anecdote, and reflection. Control participants discussed general camp experiences. Boys who received the intervention (vs. control) had more positive perceptions of girls' STEM ability, d = 0.23, an effect stronger among younger boys. These findings highlight the importance of engaging elementary-school-aged boys to make STEM climates more inclusive.

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.

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 score0.165
Threshold uncertainty score0.620

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.164
GPT teacher head0.377
Teacher spread0.214 · 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