Bibliographic record
Abstract
The Lega Cooperatives Italy has the largest number of workers' cooperatives in the Western world, and the largest fraction of the workforce employed by such firms. Ammirato (1996: 319) reports that in 1989 there were 10,445 workers' cooperatives affiliated with one of four national federations, along with many others not so affiliated. This is likely to be an underestimate because cooperatives in the agricultural, housing, transport, and fishing sectors have been excluded along with so-called “mixed” coops. But data on the Italian coops are notoriously bad (Oakeshott, 1978; Zevi, 1982; Earle, 1986: 63–66), with Earle remarking that nobody really knows “how many co-ops are genuine and operative, and how many are dormant, embryonic, phantasmal or bogus” (1986: 203). For example, entirely conventional firms have sometimes registered as cooperatives in order to gain access to public subsidies. All numerical estimates must therefore be taken with a generous serving of salt. By contrast with the plywood cooperatives and Mondragon, which developed with little state involvement, the Italian cooperative movement was heavily politicized from the outset, and has enjoyed tax advantages as well as preferential access to public land and contracts, job creation programs, loans and grants, and the financial expertise of public banking and research institutions. The national federations provide member cooperatives with more support than the plywood coops ever derived from their industry association, but have less formal authority than the central agencies of the Mondragon group.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".