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Record W3036829392 · doi:10.24908/pceea.vi0.14194

TEAMWORK TRAINING AS A MEANS OF MASTERING MORE EQUITABLE, DIVERSE, AND INCLUSIVE PRACTICES IN ENGINEERING CURRICULA

2020· article· en· W3036829392 on OpenAlexafffundvenueabout
Richard R. Chromik, Diane Dechief, Denzel Guye, Faye Siluk, Cathryn Somrani

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsMcGill University
FundersMcGill University
KeywordsTeamworkCapstoneEquity (law)CurriculumInclusion (mineral)Diversity (politics)Medical educationTraining (meteorology)Engineering managementCapstone courseEngineering educationPsychologyEngineeringEngineering ethicsKnowledge managementMathematics educationPedagogyComputer scienceManagementSociologyPolitical scienceMedicineGeography

Abstract

fetched live from OpenAlex

Survey results and student feedback from the initial year of McGill University’s E-IDEA (Engineering Inclusivity, Diversity and Equity Advancement) teamwork initiative demonstrate that undergraduate engineering students value this team-based, applied training in equity, diversity, and inclusion (EDI). The course-based training provides a critical foundation from which to build strong teamwork skills. Our findings demonstrate the benefit of initiating teamwork-integrated EDI training early in students’ programs and continuing until final capstone courses.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.088
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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)0.0000.000

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 head0.023
GPT teacher head0.262
Teacher spread0.238 · 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.

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

Citations7
Published2020
Admission routes4
Has abstractyes

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