MétaCan
Menu
Back to cohort
Record W7092213592 · doi:10.1080/19376812.2025.2574343

Climate-related information sources and the adaptation decisions of women crop farmers in the Guinea Savannah Zone of Ghana

2025· article· en· W7092213592 on OpenAlexaff

Bibliographic record

VenueAfrican Geographical Review · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Innovations
Canadian institutionsAthabasca UniversityUniversity of Calgary
Fundersnot available
KeywordsAdaptation (eye)CropCrop productionNew guineaAgriculture

Abstract

fetched live from OpenAlex

This study examines women crop farmers’ perception of climate variability and change, access to climate-related information, and their farming practices. Set in the Lawra Municipal District of the Upper West Region, the study uses data from surveys with 240 women farmers, complemented by focus group discussions and key informant interviews. The results reveal that women crop farmers generally observed significant temperature and rainfall changes consistent with long-term climate data. They identified human activities such as deforestation and poor agricultural practices as primary causes of climate change. However, a few cited other causes such as ‘the wrath of God’ or ‘natural phenomena,’ highlighting a need for improved climate education. Sources of climate information varied by community, with non-governmental organizations (NGOs), radio, family and friends, and farmer groups, being the most prominent. Notably, access to information was influenced by demographic factors such as age, education, and marital status. Financial constraints and inadequate storage facilities emerged as significant barriers. This study highlights the critical need for targeted education and interventions to enhance climate information accessibility and support adaptive capacity among women farmers. Addressing gender-specific barriers and improving resource access can enhance resilience and sustain livelihoods in the face of climate variability and change.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.020
GPT teacher head0.240
Teacher spread0.220 · 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 designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAfrican Geographical ReviewSame topicMedical History and InnovationsFrench-language works237,207