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Record W3209984175

May Business Mentors Act as Opportunity Brokers and Enablers Among University Students

2016· article· en· W3209984175 on OpenAlexaffabout
Étienne St-Jean, Maripier Tremblay, Frank Janssen, Jacques Baronet, Aziz Nafa, Christophe Loué

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversité de SherbrookeUniversité LavalUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsIdentification (biology)Business opportunityPublic universityElement (criminal law)Public relationsHigher educationBusinessProcess (computing)Political scienceMarketingPublic administrationComputer science
DOInot available

Abstract

fetched live from OpenAlex

Networks are recognized as a central component of the entrepreneurial process, in particular with regard to opportunity identification and exploitation. In this study, we specifically analyze the role of mentors who are in business as opportunity brokers and enablers among university students with entrepreneurial intentions. Our investigation on 1022 students from 13 French-language universities based in Canada, France, Belgium and Algeria indicates that mentors in business, contrary to other mentors, support opportunity identification and exploitation among university students. Although student gender, entrepreneurial experience and education have a more pronounced effect, mentoring is the only element that can be controlled for through the creation of formal support programs. These results call on public authorities, and universities in particular, to implement formal mentoring programs to support students who are interested in starting their own business, and who would not otherwise have access to business mentors in their environment.

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)0.003
metaresearch head score (Gemma)0.013
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.220
Teacher spread0.210 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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Same venueSSRN Electronic Journal→Same topicEntrepreneurship Studies and Influences→French-language works237,207→