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Record W4380985983 · doi:10.1016/j.heliyon.2023.e17345

Cooperatives and sustainability: The case of maize producers in the plateaux region of Togo

2023· article· en· W4380985983 on OpenAlexaff
Koudima Bokoumbo, Simon Berge, Kuawo Assan Johnson, Afouda Jacob Yabi, Rosaine N. Yegbemey

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSustainabilityCitizen journalismBusinessQuality (philosophy)AgricultureSocial sustainabilityMarketingSustainability organizationsSustainable developmentPolitical scienceGeographyEcology

Abstract

fetched live from OpenAlex

This study analyzed the influence of the producer's organizational form (individual or cooperative) on the three dimensions (economic, social and environmental) of sustainability in the Plateaux Region of Togo. An innovative approach called Deep Participatory Indicator-Based (DPIB) was used to target the analysis at the producer local level. The environmental sustainability score was above average for individual producers compared to cooperatives. Economic sustainability score is not related to the producer's organization form. Social sustainability was not dependent on the form of organization. The analyses led to participatory planning and actions based on three cooperative principles. Actions based on the seventh cooperative principle - Concern for Community - raise awareness among cooperators producers on the importance of carrying out social works, agro-ecological practices and sustainable agriculture for community members. The actions related to the fifth and sixth cooperative principles - Education, Training & Information and Cooperation among Cooperatives, strengthen the capacities of cooperatives on the need to seek higher quality markets and inform coops in the region about opportunities for combined marketing actions.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.083

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.270
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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