Effects of ancestral populations on entrepreneurial founding and failure: private liquor stores in Alberta, 1994-2003
Bibliographic record
Abstract
Until 1993, all liquor stores in the Canadian province of Alberta were government owned and run. In the fall of 1993, the provincial government exited liquor retailing, all government stores were shut down, and entrepreneurs were allowed to open private liquor stores. In this article, we take advantage of this abrupt regulatory change in the Alberta liquor-retailing industry to address two related issues that have received little empirical attention. First, we investigate how an ancestral population affects processes of legitimation and competition. While density-dependence theory predicts that legitimation effects outweigh competitive effects at low levels of density, will this be the case when a new population replaces a similar, ancestral one? Second, beyond density dependence, we investigate how ancestral populations affect the locations of new entrepreneurial ventures. Will entrepreneurs follow ancestral location patterns, and will these ancestral locations confer survival advantages? To answer these questions, we map all private liquor stores on the street map of Calgary, Alberta's largest city, from 1994 to 2003 and analyze store founding and failure. We use our results to draw implications for theories of density dependence, entrepreneurial behavior, and industry spatial structure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".