Cooperative Development: Sustainability Agricultural Planning Viewed Through Cooperative Equilibrium Management Theory in Togo, Africa
Bibliographic record
Abstract
Cooperative economics looks at market failures as areas for development. The cooperative development process, however, requires member engagement or cohesion in the process according to the Cooperative Management Equilibrium Theory. This cohesion requires an awareness and understanding by the cooperative members of the market failure to develop the capacity to address the failure. This article looks at the effects of government agricultural programs on economic, environmental and social sustainability. The questions we ask is how does a focus on economic development push against social and environmental sustainability within the agricultural sector in Togo? Does member cohesion within a cooperative represent a form of Polanyian double movement through social and environmental cohesion? The current development models utilize what Sen refers to as an austere mode of development which forgoes social or environmental considering them luxuries. Does the focus of economic development build capacity only for economic performance within the Togo agricultural sector at the expense of social and environmental sustainability? Utilizing Deep Participatory Indicator Approach (DPIB) approach this paper examines the economic, environmental and social indicators within two prefectures in the Plateaux Region of Togo. Indicators were separated to show the differences between individual or cooperative producers. As cooperatives it was anticipated that a greater emphasis on social and environmental sustainability would be created through cohesive social action. This study found that the emphasis on economic development included in government programs built development capacity within cooperatives emphasizing their cooperative market cohesion.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".