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Record W2785203140 · doi:10.1142/s0218495817500108

Are They Really Different: The Entrepreneurial Process from the Perspective of First and Second Generation Immigrant Entrepreneurs

2017· article· en· W2785203140 on OpenAlexaffabout
Maria El Chababi, Samia Chreim, Martine Spence

Bibliographic record

VenueJournal of Enterprising Culture · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversity of OttawaHEC Montréal
Fundersnot available
KeywordsImmigrationMainstreamPerspective (graphical)EntrepreneurshipContext (archaeology)Settlement (finance)SociologyFirst generationBusinessMarketingPolitical scienceGeography

Abstract

fetched live from OpenAlex

There is limited research on similarities and differences in entrepreneurial experiences of first and second generation immigrant entrepreneurs. Using in-depth interviews with Lebanese entrepreneurs in two Canadian cities, we analyze how entrepreneurs belonging to two different generations of immigrants experience and enact opportunity identification and assessment, and business development and operation. The analysis shows that first and second generation immigrant entrepreneurs diverge in their views of macro-institutional structures (such as regulation), risk, trust, and the role of divine providence in the entrepreneurial venture. The findings highlight the importance of understanding how first generation immigrant entrepreneurs’ past frames — developed in the pre-migration context — interact with the environment in the country of settlement in shaping entrepreneurial undertaking. The study also highlights second generation immigrant entrepreneurs’ perceived similarities to and differences from mainstream entrepreneurs. Implications for research and policy 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)0.005
metaresearch head score (Gemma)0.005
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 score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0090.004
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.293
Teacher spread0.272 · 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

Citations18
Published2017
Admission routes2
Has abstractyes

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