Bibliographic record
Abstract
Cooperatives are of particular interest to economists because of their unique ownership structure and the incentives this structure creates. In addition to the so-called property rights problems (e.g., free-rider, horizon, and portfolio problems), the analysis of agricultural cooperatives has focused on issues of market power, agency, product quality, and increasingly producer and consumer heterogeneity. These last three elements are important features of the industrialization of the agrifood system. This article highlights the key concepts required for examination of cooperatives now and in the future and incorporates these concepts into a framework that can be used to examine the myriad situations and problem settings in which agricultural cooperatives are likely to be found. A key finding of the paper is that the procompetitive and distributional impacts of cooperatives depend critically on the sensitivity of price in the downstream retail market, the nature of the cooperative’s governance structure, and the open or closed nature of cooperative membership. The article also provides a discussion of new areas in which an understanding of cooperatives and collective action would be valuable, as well as a discussion of the applicability of the proposed framework to these areas.
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 | 0.005 | 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 | 0.003 | 0.010 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".