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Record W2803895941 · doi:10.5539/sar.v7n3p71

Transitions in Cooperative Labour and the Constraints to the Adoption and Scaling-Up of Labour Intensive Agricultural Technologies

2018· article· en· W2803895941 on OpenAlexafffundvenueabout
David Natcher, Erika Bachmann, Mohamed Nasser Baco, Suren Kulshreshtha, Jeremy Pittman, Derek Peak

Bibliographic record

VenueSustainable Agriculture Research · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsUniversity of WaterlooUniversity of Saskatchewan
FundersInternational Development Research Centre
KeywordsDisadvantageLivelihoodAgricultureProduction (economics)BusinessSubsidyIndigenousWork (physics)InequalityEconomicsAgricultural productivityLabour economicsNatural resource economicsPolitical scienceMarket economyGeography

Abstract

fetched live from OpenAlex

The research presented in this paper stems from a collaboration between researchers in the Benin Republic, Nigeria and Canada who are examining the opportunities to enhance the sustainable production of under-utilized indigenous vegetables through the micro-dosage of synthetic fertilizer. Because micro-dosing is a labour intensive technology, and is time sensitive in application, we sought to better understand how the availability of labour, as affected by changes in cooperative networks, might affect adoption and scaling up opportunities. The systems of cooperative labour described in this paper reflect the culture and traditions of the Betammaribe people, residing in the village of Koumagou B in northwest Benin. Our results indicate that cooperative labour systems among the Betammaribe are in transition and are being influenced by seasonal migration, the financial demands of formal education, the use of oxen by those with relative wealth, and off-farm employment. These pressures have led to the atomizing of Koumagou B households and a concomitant decline in the availability of cooperative labour. Interventions designed to improve the livelihoods of smallholder farmers must not inadvertently perpetuate social and economic inequalities or disadvantage those most vulnerable. It is this possibility that warrants careful consideration as we contemplate the benefits of adopting and scaling-up new agricultural technologies in the future.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.281
Teacher spread0.263 · 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 designTheoretical or conceptual
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

Citations2
Published2018
Admission routes4
Has abstractyes

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