Bibliographic record
Abstract
This special issue features 14 new research papers investigating the role of farmers’ organizations (e.g., collective action, self-help groups, producer companies/organizations, and cooperatives) in supporting sustainable development. The key findings include: (1) farmer groups and cooperatives promote farmers’ adoption of good farm management practices, new agricultural technologies and sustainable farming practices, although not substantially improving farm yield; (2) outsourcing services provided by agricultural cooperatives help to increase the technical efficiency of crop production; (3) cooperative membership enhances members’ bargaining power and enables them to sell their products at higher prices; (4) cooperatives motivate rural laborers to work in off-farm sectors, while self-help groups empower rural women in decision-making; (5) internet use improves agricultural cooperatives’ economic, social, and innovative performances; (6) direct administrative intervention supporting cooperative development may lead to the emergence of shell cooperatives; (7) participation in forest farmer organizations enables wood value chain upgrading; (8) increasing the cooperative size in terms of income, equity, and assets increases the profitability of savings and credit cooperatives; and (9) creating cross-border cooperation between cooperatives generates benefits for all parties involved. These findings can inspire the design of policies aimed to support farmers’ organizations in achieving sustainable development goals.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.874 | 0.827 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".