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Record W2805655072 · doi:10.5539/jas.v10n7p390

Gender Effect on Adoption of Selected Improved Rice Technologies in Ghana

2018· article· en· W2805655072 on OpenAlexvenueno aff
Monica Addison, Kwasi Ohene-Yankyera, Robert Aidoo

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
FundersAfrican Development Bank Group
KeywordsDeskBusinessGovernment (linguistics)AgricultureVariety (cybernetics)Face (sociological concept)Economic growthAgricultural economicsGeographyEconomicsPolitical scienceSociologySocial science

Abstract

fetched live from OpenAlex

The study sought to test the hypothesis that gender influences adoption of innovations in the rice sector of Ghana. There is an existence of gender gap in adoption of farm innovations in Ghana. After desk review, it was found that the existing literature has not provided a clear linkage between gender and adoption of agricultural technologies. Thus, the objective of this study was to determine how the interaction between gender and other socio-economic factors influence the incidence of adoption of improved rice variety and fertilizer. Drawing on 917 face-to-face interviews with rice producers, the results show that child care and limited access to land inhibit female incidence of adoption. It is recommended that the innovation system should take cognizance of female reproductive role and develop, as much as possible, technology options that rely less on intensive use of labour. Furthermore, government should facilitate the development of land markets to improve female access to land, especially in northern Ghana where cultural norms restrict women’s access to land.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.022
GPT teacher head0.259
Teacher spread0.238 · 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 designBench or experimental
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

Citations14
Published2018
Admission routes1
Has abstractyes

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