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Record W2944386265 · doi:10.15353/pced.v18i0.93

Saving Small Business: The Urgent Need for Improved Business Succession Planning and how Immigrant Entrepreneurs can Help

2019· article· en· W2944386265 on OpenAlexvenueaboutno aff
Sarah V. Wayland

Bibliographic record

VenuePapers in Canadian Economic Development · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsSuccession planningEcological successionSmall businessImmigrationBusinessClosure (psychology)Business planMatching (statistics)MarketingBusiness risksBusiness developmentEconomic growthFinanceMarket economyEconomicsPolitical science

Abstract

fetched live from OpenAlex

Small business is the backbone of the Canadian economy, yet fewer than half of small and medium-sized enterprises (SMEs) in Canada currently have a succession plan in place. As such, many of these businesses could be at risk of closure, potentially reducing the wealth of the business owners in question and depriving communities of needed goods and services. This paper explores the possibility of business succession matching programs, with a focus on immigrants as potential purchasers of businesses. Immigrants are more likely to own a business than their Canadian-born counterparts, and a succession matching program could enable them to access established businesses, mentoring, and even creative financing to enhance their own chances of success as well as preserving desirable firms. The research is based on a review of existing literature, case studies and several interviews which identify an urgent need and potential solutions. Keywords: succession planning, small enterprise, immigration, immigrant entrepreneurs

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.005
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.012
GPT teacher head0.192
Teacher spread0.180 · 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 designTheoretical or conceptual
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

Citations1
Published2019
Admission routes2
Has abstractyes

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Same venuePapers in Canadian Economic DevelopmentSame topicEntrepreneurship Studies and InfluencesFrench-language works237,207