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Record W3003094031 · doi:10.24908/pceea.vi0.13701

ENGINEERS-IN-RESIDENCE PROGRAMS AS A FRAMEWORK FOR INDUSTRY ENGAGEMENT IN UNDERGRADUATE ENGINEERING EDUCATION: CHALLENGES AND OPPORTUNITIES

2019· article· en· W3003094031 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.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2019
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Curriculum Development
Canadian institutionsUniversity of Prince Edward IslandMcMaster UniversityUniversity of WaterlooUniversity of Manitoba
Fundersnot available
KeywordsLeverage (statistics)Engineering educationResidenceEngineering ethicsInstitutionPerspective (graphical)Engineering managementMedical educationEngineeringSociologyPedagogyPolitical scienceMedicineComputer science

Abstract

fetched live from OpenAlex

Industry engagement in undergraduate engineering education is a community-centred approach to learning that is hands-on and links the engineering theory to practice. This paper provides a review of existing Engineer-in-Residence (EIR) programs in Canada, including the University of Manitoba, Dalhousie University, University of Calgary, Ryerson University, University of Ottawa, and the University of Waterloo, as well as a brief international scan. We consider the motivations behind the institutions’ initiative to introduce EIR programs, different types of engagements, challenges, and opportunities. Programs are also examined externally relative to professional residency programs in business schools, among others, and relative to other forms of industry engagement in undergraduate engineering education. A brief overview of the history and role of EIRs within engineering programs is also presented. The paper will be of interest to those exploring a similar industry engagement framework at their institution, and offers a forward-looking perspective on ways to leverage the skills and experience of practicing engineers in preparing students to tackle the challenges of the future.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.234
Teacher spread0.212 · 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