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Record W3025711864 · doi:10.1504/ijesb.2017.10003382

International entrepreneurship research: how it evolved and directions for the future

2017· article· en· W3025711864 on OpenAlexaboutno aff
Léo‐Paul Dana

Bibliographic record

VenueInternational Journal of Entrepreneurship and Small Business · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsInternationalizationEntrepreneurshipInternational businessWrightSmall businessMarketingBusinessPublic relationsEconomicsManagementPolitical scienceInternational tradeFinanceEngineering

Abstract

fetched live from OpenAlex

On 28 August 2015, I received an email stating, "I am so sorry to have to tell you that Richard passed away in his sleep last night. He so treasured your friendship and collaboration." Professor Richard W. Wright (BA'61; MBA'63; PhD'70) co-founded the McGill Conferences on International Entrepreneurship, aimed at integrating the fields of international business and small business/entrepreneurship. This article is dedicated to him. In the past, internationalisation was a challenging option, usually adopted by large firms. Changes in technology now allow small firms to internationalise more easily than ever, and where there is a small domestic market, internationalisation is sometimes not an option; it has become a necessity. Membership in a network allows a small firm to internationalise in a cooperative fashion, without the need for large expenditures. The practice of international entrepreneurship thus presents a challenge to some classic theories. Internationalisation need not be undertaken incrementally. Furthermore, small companies can internationalise without transferring resources abroad, thereby avoiding the issues formerly faced during internationalisation, e.g.: 1) opportunity cost of resources transferred abroad; 2) creation of a disadvantage by resources transferred abroad; 3) the lack of resources required to operate efficiently abroad.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0030.002
Open science0.0020.001
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 head0.090
GPT teacher head0.317
Teacher spread0.227 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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