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Record W7144390048 · doi:10.15002/00026033

Building Business Partnerships for Women Entrepreneurs to Expand into Overseas Markets : A Case Study of an Asia Pacific Foundation of Canada Business Mission

2022· article· en· W7144390048 on OpenAlexaboutno aff
Mimiko Suzuki, みみこ 鈴木

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsWomen entrepreneursGeneral partnershipAsia pacificFoundation (evidence)International businessBusiness analysisBusiness ecosystemElectronic businessChina

Abstract

fetched live from OpenAlex

This study aims to address two research questions: "What are the factors that make women entrepreneurs’ overseas business partnership successful?" and "What elements are essential to women entrepreneurs’ success in doing business overseas?" It uses the Asia Pacific Foundation of Canada’s “First Canadian Women-Only Business Mission to Japan” in 2019 as a case study. A survey and interviews were conducted with the women entrepreneurs who participated in the mission, and an interview was conducted with the project leader. Results indicated that participation in overseas business missions is the most advantageous way to create new networks and expand business into overseas markets. A process for building business partnerships among women entrepreneurs was proposed based on study findings and the Canadian Women International Network (CanWIN) ecosystem. It was discovered that this business ecosystem has the potential to expand overseas opportunities for women entrepreneurs and promote economic growth.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.216
Threshold uncertainty score0.434

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0280.005
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.311
Teacher spread0.271 · 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
Published2022
Admission routes1
Has abstractyes

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