MétaCan
Menu
Back to cohort
Record W2911661682 · doi:10.1080/01436597.2018.1552076

Farmer resistance to agriculture commercialisation in northern Ghana

2019· article· en· W2911661682 on OpenAlexafffund
Siera Vercillo, Miriam Hird‐Younger

Bibliographic record

VenueThird World Quarterly · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of TorontoWomen's and Gender Studies et Recherches FéministesWestern University
FundersSocial Sciences and Humanities Research Council of CanadaInternational Development Research Centre
KeywordsResistance (ecology)AgriculturePolitical scienceGeographyAgricultural economicsEconomicsAgronomyBiologyArchaeology

Abstract

fetched live from OpenAlex

Drawing on postcolonial literature and theories of farmer resistance, this article provides an empirically based alternative explanation of African farmer behaviours to narratives that blame them for their lack of technology adoption. Based on six months of ethnographic immersion in one district in the Northern Region of Ghanaa, we identify the ways that farmers defy commercial agriculture investment, government services and non-governmental organisation (NGO) project interventions aimed at intensification, and describe their reasons for doing so. This study interprets farmers’ acts of defiance, such as side-selling or falsely weighting their products, as insights into everyday acts of resistance. We find that throughout Ghana’s postcolonial period, agriculture intensification policy and practice have produced an environment where various development actors and farmers have both a sense of entitlement and mistrust of each other. Farmers’ acts of sabotage may be spaces where they make rational choices based on experiences of historical antecedence, including decades of failed development projects, elite corruption and mismanagement, degrading ecologies and donor hegemony.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.999

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)0.0000.001

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.

Opus teacher head0.009
GPT teacher head0.201
Teacher spread0.192 · 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.

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

Citations16
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueThird World QuarterlySame topicAgriculture, Land Use, Rural DevelopmentFrench-language works237,207