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Record W3209737204 · doi:10.1177/09636625211051970

Science communication skills as an asset across disciplines: A 10-year case study of students’ motivation patterns at Université Laval

2021· article· en· W3209737204 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

VenuePublic Understanding of Science · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsConcordia University
FundersConcordia University
KeywordsAsset (computer security)Science communicationCommunication skillsPsychologyScience educationMathematics educationMedical educationComputer scienceMedicine

Abstract

fetched live from OpenAlex

As the demand for science communication proficiency is growing and post-secondary science communication courses and programs are launched or redesigned, it is paramount to understand who takes these courses and why. Based on a convergent mixed methods approach, this article explores the characteristics and self-reported motivations of students enrolled in an online science communication course at Université Laval, Canada, from 2009 to 2018. Results show that the typical science communication student is a woman with a career-orientated motivation pattern, mostly seeing science communication skills as an asset for a career in communication, science, or health. Be it career-driven, interest-driven, or online education-driven, motivation pattern differences emerge depending on the students' gender or field of study. Those patterns offer new paths of research, such as exploring the impact of science communication program design or of advertising strategies on enrollment.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0040.003
Scholarly communication0.0000.002
Open science0.0020.001
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 head0.423
GPT teacher head0.479
Teacher spread0.056 · 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