Making Succession a Success: Perspectives from Canadian Small and Medium-Sized Enterprises
Bibliographic record
Abstract
SMEs are at the heart of the Canadian economyand social structure. However, researchers worry about the impact of an agingpopulation on small- and medium-sized firms and their succession. This analysisdiscuss the results and implications of a survey conducted by the CanadianFederation of Independent Business (CFIB) in 2004, which included data from4,311 CFIB members. The key issues the survey focused on included: (1) when the owners expect toend their businesses; (2) how they prepare for succession; and (3) the barriersthey face in implementing their plans for success. The findings show that 41%of SME owners intend to exit their business after 5 years; only 1/3 have a planto sell, transfer, or wind down their business in the future. The findings also show that accountants and lawyers are the two most commontypes of professional or technical assistance used in developing asuccession plan, while the most common barriers to succession aresoft in nature. Four major research gaps are identified in theliterature about succession planning: (1) the need for a mapping of whatmotivates business owners to plan ahead; (2) the overall impact of failedbusiness succession in Canadian economy; (3) the challenges faced by both thegovernment and the financial community in facilitating business succession; and(4) the perspective of future successors of business. (CBS)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.044 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".