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Entrepreneurial Process from the Perspective of Different Generations of Immigrant Entrepreneurs

2015· article· en· W2779180077 on OpenAlexaffabout
Maria El Chababi, Samia Chreim, Martine Spence

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsImmigrationEmbeddednessGrounded theoryEntrepreneurshipPerspective (graphical)Qualitative researchSociologyConceptual modelProcess (computing)BusinessPolitical scienceSocial scienceEpistemologyComputer science

Abstract

fetched live from OpenAlex

Recent research on immigrant entrepreneurship affirms that immigrants should not be treated as one entity, and thus attention has been directed towards groups of immigrant entrepreneurs that were previously neglected in the literature. One such group is the second generation children of immigrants. There is limited research on similarities and differences in the entrepreneurial experiences of first and second generation immigrant entrepreneurs. This paper uses the mixed-embeddedness approach to develop understanding of how different generations of immigrant entrepreneurs experience the entrepreneurial process. Using a grounded theory approach and qualitative in-depth interviews with Lebanese immigrant entrepreneurs in two large cities in Canada, we propose two conceptual models grounded in the data. The first portrays the similarities and differences in the micro and macro level enablers/obstacles experienced by first and second generation immigrant entrepreneurs, and the second portrays how the two different generations experience the three stages of the entrepreneurial process. Implications for theory and for future research are addressed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.004
Scholarly communication0.0060.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2015
Admission routes2
Has abstractyes

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