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Record W4375848298 · doi:10.58970/ijsb.2122

The Rise of Chinese Entrepreneurs in Canada: From Immigrant to Influencer

2023· article· en· W4375848298 on OpenAlexaboutno aff
Peng S un, Xiaode Zuo

Bibliographic record

VenueInternational Journal of Science and Business · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsImmigrationDiversity (politics)Thematic analysisCultural diversityNonprobability samplingQualitative researchBusinessMarketingPublic relationsSociologyPolitical scienceSocial sciencePopulation

Abstract

fetched live from OpenAlex

This study aims to examine the influence of Chinese immigrants on Canadian business culture and the role of Chinese business associations in shaping the Canadian business landscape. A qualitative research design was used to explore the topic, and semi-structured interviews were conducted as the primary method of data collection. 25 participants were selected for the study through purposive sampling, including Chinese immigrant business owners, employees of Chinese-owned businesses, and members of Chinese business associations in Canada. Thematic analysis was used to analyze the data collected through the interviews and identify patterns and themes that emerged from the participants’ responses. The study found that Chinese immigrants have had a significant influence on Canadian business culture and that Chinese business associations play an important role in promoting business cooperation between different cultural groups. The results also suggest that embracing cultural diversity in the business environment has both benefits and challenges, and businesses must effectively manage cultural diversity in the workplace to promote cooperation between different cultural groups. This study provides valuable insights into the ways in which Chinese immigrants have shaped the Canadian business landscape and the importance of embracing cultural diversity in the business environment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.381

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.290
Teacher spread0.277 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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