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Record W2782348562 · doi:10.1108/neje-18-02-2015-b002

The impact of immigrant entrepreneurs℉ social capital related motivations

2015· article· en· W2782348562 on OpenAlexaff
Claudia Gomez, B. Yasanthi Perera, Judith Y. Weisinger, David H. Tobey, Taylor Zinsmeister-Teeters

Bibliographic record

VenueNew England journal of entrepreneurship · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsBrock University
Fundersnot available
KeywordsSocial capitalImmigrationEthnic groupEntrepreneurshipEthnic communityCapital (architecture)Individual capitalBusinessSociologyDemographic economicsEconomic growthPolitical scienceFinancial capitalEconomicsHuman capitalSocial scienceFinanceGeography

Abstract

fetched live from OpenAlex

The immigrant entrepreneurship literature indicates that immigrant entrepreneurs reap numerous benefits from their co-ethnic communities℉ social capital. These benefits, however, often come at a price because scholars note the potential for this community social capital to impose limitations on the entrepreneurs. While the literature largely focuses on the benefits of social capital, there is no research on what motivates the immigrant entrepreneurs to engage with their co-ethnic community in terms of contributing to, and utilizing, their co-ethnic communities℉ social capital, and the consequences these may have on their enterprises. Addressing this gap in the literature is important in the development of successful immigrant enterprises. Thus, based on a model posited by Portes and Sensenbrenner (1993), we suggest that immigrant entrepreneurs℉ motivations will influence their use of, and contributions to, co-ethnic community social capital, impacting, in turn, business success. We contribute to both the immigrant entrepreneurship and social capital research through exploring how entrepreneurs℉ motives, with respect to their co-ethnic communities℉ social capital, influence business success.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.258
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.037
GPT teacher head0.305
Teacher spread0.268 · 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

Citations23
Published2015
Admission routes1
Has abstractyes

Explore more

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