Policy Supports for Co-operative Development: Learning from Co-op Hot Spots
Bibliographic record
Abstract
The international co-operative movement has prioritised supportive legal frameworks as a key constituent of co-operative growth (ICA, 2013). Unfortunately, there is not a robust literature on co-operative policy to help meet this need. Supportive legal frameworks for co-operatives are a “deeply under-researched area” (Adeler, 2014: 50). We recently conducted a review of the existing research and found that despite its dearth, the literature points to six primary forms of policy support that have been successfully deployed internationally to support co-operative growth: co-operative recognition, financing, sectoral financing, preferential taxation, supportive infrastructure, and preferential procurement. The most developed examples of these policies are found in areas of dense co -operative concentration, or “co-op hot spots”: the Basque region of Spain, Emilia Romagna in Northern Italy, and Quebec, Canada. This article accounts for how these six policy forms appear in the co-operative dense regions. The aim of this analysis is to facilitate further research in the understudied area of co-operative policy, and to clarify policy successes for organisers in the co-operative movement interested in emulating them.
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 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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.028 | 0.040 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".