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Record W1968675896 · doi:10.1093/icc/dtr018

Effects of ancestral populations on entrepreneurial founding and failure: private liquor stores in Alberta, 1994-2003

2011· article· en· W1968675896 on OpenAlexafffundabout
Glen Dowell, Richard J. David

Bibliographic record

VenueIndustrial and Corporate Change · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsMcGill University
FundersGovernment of Alberta
KeywordsLegitimationGovernment (linguistics)Competition (biology)PopulationEmpirical evidenceBusinessEconomic geographyEconomicsPolitical scienceLawPoliticsSociologyDemographyBiologyEcology

Abstract

fetched live from OpenAlex

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.190
GPT teacher head0.240
Teacher spread0.050 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations15
Published2011
Admission routes3
Has abstractyes

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