MétaCan
Menu
Back to cohort
Record W2182136507 · doi:10.36510/learnland.v3i2.352

How Science Clubs Can Support Girls’ Interest in Science

2010· article· en· W2182136507 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.

venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLEARNing Landscapes · 2010
Typearticle
Languageen
FieldPsychology
TopicScience Education and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsStereotype (UML)Affect (linguistics)PsychologyScience and engineeringStereotype threatAssociation (psychology)Women in scienceSocial psychologySociologyEngineeringEngineering ethicsGender studiesCommunication

Abstract

fetched live from OpenAlex

Enter any classroom across Canada, ask children to describe a scientist, and you will likely hear about brilliant, but crazy old men in lab coats and goggles doing dangerous experiments (the mad scientist). Stereotypes such as this, however, can affect an individual’s likelihood to take science courses, and the attention he or she gives to the studies of the sciences.The Canadian Association for Girls in Science (CAGIS) attempts to break the scientist stereotype, and to facilitate interest and confidence in science, technology, engineering, and mathematics (STEM) by holding regular events with fun, hands-on activities led by women and men in STEM-related fields.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.052
GPT teacher head0.360
Teacher spread0.307 · 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