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Record W1895116946 · doi:10.53228/njas.v19i1.213

Farmer perceptions on indigenous pig farming in Kakamega district, western Kenya

2010· article· en· W1895116946 on OpenAlexaff
Florence Mutua, Samuel Arimi, William Ogara, Cate Dewey, Esther Schelling

Bibliographic record

VenueCGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research) · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIndigenousSwahiliBreedFocus groupAgricultureKenyaSocioeconomicsGeographyEconomic growthPig farmingPolitical scienceAgricultural scienceBusinessMarketingAnimal productionSociologyBiologyAnimal scienceEconomics

Abstract

fetched live from OpenAlex

Objectives for this paper were to: study farmer beliefs and perceptions on local pig farming practices; and to explore opportunities for improved located production in selected villages of Western Kenya. The paper seeks to understand why the local pig breed still remains the predominant breed in these areas despite numerous calls to introduce better exotic breeds. Most pigs in Kenya are of exotic breeds, intensively managed on commercial farms. Focus group discussions were used to gather data. Discussions were taped, transcribed and translated from Swahili to English. Farmers use pigs to guard homes at night, pigs also act as a charm to protect families against evil spirits. Women farmers manage the family pigs, men sell the pigs. Farmers identified feeding, marketing, and breeding as the main challenges affecting the sector. The discussions identified a number of opportunities for improved production, and likely strengthened the bond between the farmers, researchers and staff. This created an outlook that can now be used in further public engagement as ongoing research studies on appropriate feed, health and improvement of market access are being analysed.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.911
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.002
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 head0.070
GPT teacher head0.355
Teacher spread0.284 · 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

Citations52
Published2010
Admission routes1
Has abstractyes

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