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Record W2741529088 · doi:10.1108/et-12-2016-0183

The online promotion of entrepreneurship education: a view from Canada

2017· article· en· W2741529088 on OpenAlexaffabout
Roger Pizarro Milian, Marc Gurrisi

Bibliographic record

VenueEducation + Training · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsMindsetEntrepreneurshipOriginalityPromotion (chess)Entrepreneurship educationValue (mathematics)SociologyHigher educationPublic relationsMarketingContent analysisPedagogyPolitical scienceBusinessSocial scienceEconomic growthEconomicsQualitative research

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to empirically examine how entrepreneurship education is being marketed to students within the Canadian university sector. Design/methodology/approach A content analysis of the webpages representing 66 entrepreneurship education programs in Canada is performed. Findings Entrepreneurship education is found to be framed as providing students with a collaborative learning experience, useful hands-on skills with real world applications and an entrepreneurial mindset. Research limitations/implications This study looks at only one type of promotional material, and thus, further research is needed to triangulate its findings. Originality/value This is the first study that empirically examines the marketing of entrepreneurship education in Canada.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.820

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0130.003
Scholarly communication0.0070.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.056
GPT teacher head0.288
Teacher spread0.232 · 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 designNot applicable
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

Citations23
Published2017
Admission routes2
Has abstractyes

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