International entrepreneurship research: how it evolved and directions for the future
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.033 | 0.040 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".