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Record W2604636859 · doi:10.24908/pceea.v0i0.6521

TECHNOLOGY ENTREPRENEURSHIP @ SFU: EXPERIENTIAL, INTERDISCIPLINARY LEARNING THROUGH AN IMMERSIVE TWO-YEAR PROGRAM

2017· article· en· W2604636859 on OpenAlexafffundvenue
Kevin Oldknow, Sarah Lubik

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2017
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsSimon Fraser University
FundersBritish Columbia Innovation Council
KeywordsExperiential learningEntrepreneurshipCommercializationScope (computer science)StakeholderMechatronicsProgram Design LanguageEngineeringKnowledge managementEngineering managementEngineering ethicsSociologyComputer scienceBusinessMarketingPedagogyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

This paper provides an overview of experiences to date in developing and delivering the Technology Entrepreneurship @ SFU program - an interdisciplinary program in which Mechatronic Systems Engineering and Business students at Simon Fraser University collaborate on market-driven, entrepreneurial initiatives that simultaneously satisfy their respectivecapstone project requirements. The paper includes a discussion of program scope and objectives, design, implementation and revisions. The role of the program in providing experiential, interdisciplinary learning for Engineering and Business students through an immersive experience is discussed. Qualitative and quantitative program results to date are reviewed, with program enrollment and completion trends, course evaluation data and key stakeholder metrics indicating a level of successin: (1) creating an interdisciplinary, entrepreneurial culture amongst the students, (2) preparing students for future career paths that may include entrepreneurship and (3) potential further commercialization of prototypesdeveloped in the Technology Entrepreneurship program. Longer-term measures of success are also considered and discussed in light of the experiences to date.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.168
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.007
GPT teacher head0.254
Teacher spread0.246 · 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

Citations0
Published2017
Admission routes3
Has abstractyes

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