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Record W2956289424 · doi:10.1177/2515127419856602

Stimulating Entrepreneurial Interest in Engineers Through an Experiential and Multidisciplinary Course Collaboration

2019· article· en· W2956289424 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.

Bibliographic record

VenueEntrepreneurship Education and Pedagogy · 2019
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMultidisciplinary approachExperiential learningEntrepreneurshipCohortMedical educationEngineering educationDisciplinePsychologyEngineeringPedagogyMedicineSociologyEngineering managementPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Entrepreneurship education is gaining momentum in today’s world. This article focuses on a teaching intervention introducing engineering students to multidisciplinary innovation and entrepreneurship, using experiential learning and the lean start-up method. We compare the experience and attitude change of engineers enrolled in a mixed cohort of undergraduate business and engineering students to those enrolled in an engineering-only cohort. To evaluate the effectiveness and outcome of the program, data were collected at the very beginning of each course and at their completion. Results indicated interest in entrepreneurship significantly increased at the end of the course and supported the concept that interest in entrepreneurship can be positively motivated through experiential learning. The engineering-only cohort experienced a greater change in entrepreneurial interest and were challenged more over the course of the term than the multidisciplinary cohort. Nonetheless, the multidisciplinary cohort benefited by interacting with business students and leveraging the shared disciplinary experience.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.187
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.022
GPT teacher head0.318
Teacher spread0.296 · 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