Cooperative enterprise at scale: comparative capitalisms and the political economy of ownership
Bibliographic record
Abstract
Abstract Under what conditions do cooperatively owned enterprises scale to stand alongside investor-owned firms? This article measures and attempts to explain large cooperatives’ variable prevalence across high-income capitalist democracies. Controlling for other known social, economic and geographic factors, statistical models confirm that state-mediated institutional arrangements, as operationalized through two comparative capitalism frameworks (Varieties of Capitalism and Welfare Regimes), are a significant factor in this variation. Cooperatives scale in a manner that complements arrangements in coordinated market economies, while exhibiting institutional incongruencies with those of liberal market economies and residual welfare states. Public policies which have variously enhanced or inhibited cooperatives’ ability to coordinate to scale are compared across four case countries (United States, France, Finland and New Zealand). Policy differences are shown to reflect the joint effect of state-mediated institutional arrangements alongside other control variables. They reveal how states privilege some ownership forms over others, suggesting a distinct political economy of ownership.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".