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Record W4297903828 · doi:10.55549/epess.1179326

Talent Development: Examining the Impact of University Education on Entrepreneurship

2022· article· en· W4297903828 on OpenAlexafffundabout
Jonathan PARKES, Davar Rezania

Bibliographic record

VenueThe Eurasia Proceedings of Educational and Social Sciences · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEntrepreneurshipEntrepreneurship educationPolitical sciencePsychologyEngineering ethicsEngineering

Abstract

fetched live from OpenAlex

The purpose of this research was to identify various components of university education that influence students’ venture creation and entrepreneurial behaviour. A literature review was completed to identify how university education impacts entrepreneurship. Based on a conceptual model developed, a realist evaluation was conducted to examine the relationship between university education and entrepreneurship. For the evaluation, fifteen student entrepreneurs from the University of Guelph in Ontario, Canada were interviewed to gain insight into their experience and evaluate which components of university education they found pivotal to their entrepreneurial undertaking. Interviewee responses were assessed to establish collective findings and identify elements of university education that may be modified to promote students’ entrepreneurial behaviour further. The research demonstrates direct alignment between interviewee responses and literature, identifying the promotive influence of an adapting and engaging university environment, decentralized curriculum, diversity of involvement, promotion of intrapreneurship and entrepreneurship, and contributing to community development. As a result of the interviews conducted, the study identifies two unspecified elements within the literature, collaborative education and emphasizing the application of education. These findings provide concrete insight into the impact of university education on entrepreneurship.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.614
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.044
GPT teacher head0.264
Teacher spread0.221 · 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.

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
Published2022
Admission routes3
Has abstractyes

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