The Legitimacy of Transnational Startups: The Case of Canadian-Iranian Startups
Bibliographic record
Abstract
Building legitimacy is one of the main challenges of every entrepreneur, especially transnational ones.The study has employed a combination of qualitative and quantitative methods including grounded theory and Borda count method to capture the perception of new transnational ventures (NTVs) from the sources of legitimacy for financiers and grant providers and the most important challenges of NTVs.The results from studying six Canadian-Iranian NTVs show that the most important challenges face NTVs to obtain legitimacy are "Insufficient understanding of Canadian business environment and business language", "Procuring funding", and "Building a network" Also, the most important perceived criteria of legitimacy are "the amount of investment in the business", "experience and background of the founder and director", and "credit history" of the owner(s).The contribution of the research is providing an examination of the intersection of NTVs and legitimacy.NTVs may use the results to manage their legitimacy.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".