Bibliographic record
Abstract
Over the last three decades, cooperatives experienced acceleration of institutional innovation with the introduction of many variations to the reference model. It is certainly not surprising that coops changed their organizational structure over time to face the challenges of world. In the United States and in Canada they are commonly referred to as new generation cooperatives, in Italy and Spain as cooperative groups or network of cooperatives. One of the main feature of these new organizational structures is their attempt to take some advantages of the investor oriented firms (above all in capital raising activities) while retaining the mutual/cooperative status. Many of these changes have been undertaken to facilitate the growth of the enterprises both in domestic market and abroad. Due to the wideness of the phenomenon we could name the last three decades the age of hybridization. However in some cases the search for new structures went further and assumed the aspect of conversion of mutuals into stock firms. Our paper will deal with this latter part of the story, focusing on cooperatives that preferred conversion or demutualization to hybridization. The paper describes the chronology and the geography of demutualization and analyses the forces that drove it over the last decades. The main conclusion is that demutualization provided solutions for real problems, as hybridization did, however the choice between these two options seems to have been more a matter of ideology than of efficiency.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".