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Record W4403518191 · doi:10.1177/10422587241280125

Elaborating On Ethnic Entrepreneurship: How Differences in Immigrant Founders’ Strategic Choices Regarding Human Capital Sourcing Affect Business Model Designs and Evolution

2024· article· en· W4403518191 on OpenAlexaff
Mallika Banerjee

Bibliographic record

VenueEntrepreneurship Theory and Practice · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAffect (linguistics)EntrepreneurshipImmigrationEthnic groupHuman capitalBusinessNew VenturesCapital (architecture)Economic geographyMarketingEconomicsSociologyPolitical scienceEconomic growthGeography

Abstract

fetched live from OpenAlex

Strategic human capital literature assumes founders mobilize human resources from the market. Social capital research shows that relying on nonmarket sources, such as ethnic communities, for resources results in distinct ways of organizing business activities in immigrant and nonimmigrant firms. Based on a field study, I found that the impact of sourcing human capital from the market versus the ethnic community on business model designs and evolution can be better understood by examining the nature of control firm owners exert over employees and the network segmentation dynamics of subgroups within a community. The study expands on strategic human and social capital research, identifying two boundary conditions for entrepreneurship literature.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
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 head0.097
GPT teacher head0.338
Teacher spread0.242 · 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.

Study designTheoretical or conceptual
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

Citations3
Published2024
Admission routes1
Has abstractyes

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