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Record W2910957759 · doi:10.24908/pceea.v0i0.13058

Teaching Engineering Innovation, Design, and Leadership in a Community of Practice

2018· article· en· W2910957759 on OpenAlexafffundvenueabout
Marnie Jamieson, John M. Shaw

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Alberta
FundersUniversity of Alberta
KeywordsCommunity of practiceEngineering ethicsWork (physics)EngineeringEntrepreneurshipOvertimeEngineering managementProcess (computing)SociologyPedagogyPolitical scienceComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Process design is taught by instructors with adiverse mix of industrial and academic experience. Theinstructors work in close collaboration with workingprofessional engineers including industrial technicalspecialists, entrepreneurs, and academic colleagueswith an industrial focus to prepare unique processdesign projects and to advise student teams. Many of theindustry advisors are long term contributors and overtime some have been recruited as instructors for thedesign course. This community of practice offersstudents a window on engineering design practice andinnovation as they transition to the professionalcommunity. This paper explores the contribution of thecommunity of practice to student development, theachievement of the Canadian Engineering AccreditationBoard (CEAB) graduate attributes, and the developmentof an innovation ecosystem.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0140.015
Scholarly communication0.0150.006
Open science0.0030.015
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.028
GPT teacher head0.228
Teacher spread0.200 · 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 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

Citations4
Published2018
Admission routes4
Has abstractyes

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