MétaCan
Menu
Back to cohort
Record W1567309332 · doi:10.22329/celt.v4i0.3274

10. Utilizing Science Outreach to Foster Professional Skills Development in University Students

2011· article· en· W1567309332 on OpenAlexafffundvenueabout
Edward W. Eng

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersUniversity of Toronto ScarboroughUniversity of Toronto
KeywordsOutreachCourseworkMedical educationPsychologyGeneral partnershipCreativitySkills managementProfessional developmentRelevance (law)PedagogyMedicinePolitical science

Abstract

fetched live from OpenAlex

Students seek unique experiences to obtain and enhance professional development skills and to prepare for future careers. Through the Let’s Talk Science Partnership Program (LTSPP), a voluntary science outreach program at University of Toronto Scarborough, students are given the opportunity to continually improve on skills which include: the “3 Cs” (creativity, communication, cooperation), and leadership and organization skills through hands-on activities in classrooms and community centres across the city and in isolated rural communities. Volunteers serve as mentors, and frequently transfer knowledge related to their research and coursework to youth. Here, we present results from surveys on current and past volunteers (2004-2010). Volunteers were asked to evaluate the value of the skills they obtained through science outreach, and the relevance of those skills to obtaining current work and achieving long-term career goals. Respondents commented on the effectiveness of the skills they obtained and ranked the transferable skills. We show that volunteer work through LTSPP largely improves their communication and confidence skills. As well, students identified clear links between science outreach and professional goals, and highly recommended LTSPP to others.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.317
Teacher spread0.279 · 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.

Study designQualitative
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

Citations2
Published2011
Admission routes4
Has abstractyes

Explore more

Same venueCollected Essays on Learning and TeachingSame topicService-Learning and Community EngagementFrench-language works237,207