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Record W2804686229 · doi:10.5539/jfr.v7n4p30

Understanding Traditional Meat Processing Knowledge among the Borana Pastoralist of Northern Kenya

2018· article· en· W2804686229 on OpenAlexvenueno aff
Buke G. Dabasso, Hassan G. Roba, Anselimo Makokha, Arnold N. Onyango, J.M. Maina

Bibliographic record

VenueJournal of Food Research · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPastoralismLivelihoodTraditional knowledgeConsumption (sociology)AgricultureAgricultural scienceFood processingLivestockProduct (mathematics)IndigenousBusinessMeat packing industryGeographyBiotechnologyFood scienceBiologySocial scienceMathematicsSociology

Abstract

fetched live from OpenAlex

Indigenous knowledge on food preparation is an activity practised in almost all agricultural production system. Amongst the Borana pastoralist of Northern Kenya, milk and meat production are the cornerstone of livelihood, and more often abundance occurs without possibility of immediate consumption, triggering the need to preserve surplus for future consumption. The objective of this paper is to document and understand traditional meat preparation knowledge amongst Borana pastoralist’s women of Northern Kenya.The method of collecting information included in-depth interviews and participant observations to document meat preparation skills and knowledge of Borana people as appertains to traditional food ways. It was observed that methods of traditional meat processing and preparation included different forms of drying, use of heat and storage in fat. Fourteen traditional meat products and seven preservation techniques were documented. Drying and deep frying were the major form of meat preservation. Women skillfully put a lot of effort in all stages of meat preparation to produce an end product that is not only shelf stable but traditional products that are appreciated and nutritious. It was observed that only four of the products are currently in use, an indication of steady decline in meat handling knowledge and preparation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.243
GPT teacher head0.341
Teacher spread0.098 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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