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Record W4407055923 · doi:10.1504/ijev.2024.144200

Entrepreneurial immigrants: powering innovation in Canada

2024· article· en· W4407055923 on OpenAlexaboutno aff
Masihullah Nemat

Bibliographic record

VenueInternational Journal of Entrepreneurial Venturing · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationEntrepreneurshipBusinessEconomic geographyMarketingEconomicsPolitical science

Abstract

fetched live from OpenAlex

This study investigates how immigrant entrepreneurship drives invention and socio-economic progress in Canada. The research investigation aims to fill the gaps in comprehension of the accomplishments, difficulties, and lessons learned of immigrant businesses and looks at successful policy measures as well as assistance mechanisms in Canada. In the literature review, key theories, ideas, and models related to immigrant businesses and creativity are examined, including the Triple Helix model, social capital theory, and the push-pull theory of entrepreneurship. The data are analysed using thematic analysis, which leads to key insights into immigrant businesses and their ingenuity. It offers policy recommendations based on solid research. In order to make the best use of the talents of immigrant corporate executives and contribute to Canada's economic growth via innovation, the research concludes that a favourable and welcoming entrepreneurial environment must be developed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.619

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.014
GPT teacher head0.281
Teacher spread0.267 · 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 designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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