Entry strategies and performane [sic] of new ventures in clusters and isolation
Bibliographic record
Abstract
This thesis includes three distinct manuscripts which help to elucidate the impacts of location externalities on entry strategies and performance of heterogeneous new ventures, as well as on the overall regional entrepreneurial activity. The first manuscript studies location choices of de novo entrants as a function of their initial resources and capabilities. It reveals that weak and strong entrants have distinct preferences for various location attributes reflecting both the differences in externalities they face and the value of such externalities in facilitating entry, maturation, and future prospects of profitability. The second manuscript reveals that geographical industry clustering matters to survival of new entrants. Moreover, firm specific factors and strategies which enhance survival vary significantly between different levels of industrial clustering. It shows that the initial endowments of resources and capabilities provide longer adolescence period for firms in clusters. The third manuscript argues that the causal links between entry and failure rates also flow from failure to entry. It shows that exit of local older firms stimulate entry and renewal. For the empirical analyses I used a longitudinal data set developed by Statistics Canada, which provides detailed firm level data for all firms operating in Canada from 1984 to 1998 as well as their employment, financial characteristics, industry affiliation, and location.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".