Responding to Business Succession Issues and Crises by Converting to Cooperatives
Bibliographic record
Abstract
Most of Canada’s 1.2 million small- and medium-sized enterprises (SMEs) have been affected by the pandemic, compounding serious issues facing Canada’s economy, including the potential for large-scale business closures due to the growing number of retirement-aged owners without a formal succession plan. One social economy option in Canada to save businesses and the jobs they provide is to sell them to employees or community members and convert them to cooperatives. The Conversion to Co-operatives Project set out to better understand business conversion to cooperatives (BCCs) in Canada and help the country’s co-op movement build BCC capacity. This article outlines the project’s key findings to date. RÉSUMÉ La pandémie a entraîné des conséquences sur la plupart des 1,2 millions de petites et moyennes entreprises au Canada, aggravant de sérieux problèmes pour l’économie du pays, y compris la possibilité de fermetures d’entreprises à grande échelle causées par le nombre croissant de propriétaires au seuil de la retraite qui n’ont aucun plan de relève. Au Canada, une option provenant de l’économie sociale pour sauver les entreprises et les emplois qu’elles fournissent serait de les vendre à des employés ou à des membres de la communauté et de les convertir en coopératives. Le Projet de conversion en coopératives a cherché à mieux comprendre la conversion d’entreprise en coopérative (CEC) au Canada et à aider le mouvement coopératif du pays à accroître la capacité en CEC. Cet article présente les données clés du projet à ce jour.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".