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Record W3036939852 · doi:10.24908/pceea.vi0.14123

CO-OP STUDENT INVOLVEMENT IN THE ADVANCEMENT OF A FACULTY’S SAFETY EDUCATION AND CULTURE: A LARGE-SCALE PROJECT OF VIDEO CREATION FOR LABORATORY COURSES

2020· article· en· W3036939852 on OpenAlexafffundvenueabout
Anita Parker, Nicole Dyck, R. S. Fuhrer, Jason P. Carey

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsDeliverableMindsetLaboratory safetySafety cultureScale (ratio)Medical educationWorkspaceTest (biology)EngineeringEngineering managementPedagogyPsychologyMathematics educationComputer scienceManagementMedicine

Abstract

fetched live from OpenAlex

Safety is one of many imperatives considered by engineers as they design, build, test, and maintain solutions to meet client needs. The mindset and relevant knowledge of individual engineers toward safety is formed n the tertiary classroom, which students then carry with them into their careers and influence the safety culture of heir workspaces. As an innovative means to include safety content in the University of Alberta’s Faculty of Engineering undergraduate programs, a large-scale project tasked co-op students with creating videos promoting and instructing on safety concepts and procedures in laboratory spaces. Beyond the deliverables, this authentic, active learning experience increased the safety knowledge and commitment of both students and staff, infiltrating and positively impacting the safety culture within all engineering and science programs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0040.002
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.007
GPT teacher head0.257
Teacher spread0.250 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2020
Admission routes4
Has abstractyes

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