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Record W2021857698 · doi:10.1504/jibed.2015.066746

Entrepreneurial desirability and intent among youth in Bhutan

2015· article· en· W2021857698 on OpenAlexaffabout
Dave Valliere, Steven A. Gedeon

Bibliographic record

VenueJ for International Business and Entrepreneurship Development · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEntrepreneurshipScope (computer science)Public relationsNew VenturesPolitical scienceEconomic growthMarketingBusinessPsychologyEconomics

Abstract

fetched live from OpenAlex

The Kingdom of Bhutan has embarked on an ambitious programme of entrepreneurial training for youth, with the objective of stimulating increased new venture formation and job creation. The entrepreneurship literature on the drivers of entrepreneurial intent is well–developed for the case of opportunity–seeking individuals in developed countries, but the literature around intent for necessity–based entrepreneurship in emerging countries is much less developed. This study is an exploration into entrepreneurial intent and the precursors of desirability and positive social norms affecting the career decisions of these youths. We surveyed 364 young people with an express interest in business and entrepreneurship, located in Bhutan and Canada (as a typical representative of the scope of prior research into entrepreneurial intent). Our results demonstrate higher entrepreneurial intent and more positive attitudes and social norms in Bhutan than in Canada. These results suggest that new entrepreneurship training programmes in Bhutan should be designed to focus primarily on other aspects, such as building skills and acquiring resources.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.278
Threshold uncertainty score0.553

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.057
GPT teacher head0.247
Teacher spread0.190 · 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 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

Citations5
Published2015
Admission routes2
Has abstractyes

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