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Record W4236396103 · doi:10.1177/146575030000100105

Becoming an entrepreneur: A participant's perspective

2000· article· en· W4236396103 on OpenAlexaff
W. Ed McMullan, Karl H. Vesper

Bibliographic record

VenueThe International Journal of Entrepreneurship and Innovation · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEntrepreneurshipPerspective (graphical)Process (computing)Public relationsSociologyMarketingPsychologyBusinessPolitical scienceComputer science

Abstract

fetched live from OpenAlex

A single case study of a student was elaborated upon to illustrate the process of change through education. By choosing to study a graduate who had minimal background preparation and minimal interest in entrepreneurship before the education programme, the researchers have attempted to address some of the limits of change possible through entrepreneurship education. A structured interview was used to provide the initial ‘before and after’ account, after which extended and repeated probing was employed as the primary tool for exploring the personal development process involved. The case history was then used as a basis for developing a model of personal development required to make the transition from non-entrepreneur to entrepreneur. This case study was further intended to illustrate some of the relative merits of conducting in-depth case analysis over survey research in the domain of entrepreneurship education. Without in-depth case studies of individuals it is hard to know how much entrepreneurship programmes can change individuals. The possibility remains that entrepreneurship programmes just take potential entrepreneurs and give them a few more tools. Case studies of the change process can provide educators with a more complete understanding not only of what changes are possible within the confines of an education programme, but also of what programme interventions are more likely to produce the desired changes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.801

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 studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.060
GPT teacher head0.303
Teacher spread0.243 · 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

Citations4
Published2000
Admission routes1
Has abstractyes

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