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Record W1895139718 · doi:10.3389/fvets.2015.00046

“The Maasai Need Cows and the Cows Need Maasai,” the Use of a Photovoice Approach to Assess Animal Health Needs

2015· review· en· W1895139718 on OpenAlexafffund
Frank van der Meer, Eoin Clancy, Adam Thomas, Susan Kutz, Jennifer Hatfield, Karin Orsel

Bibliographic record

VenueFrontiers in Veterinary Science · 2015
Typereview
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Calgary
FundersCumming School of Medicine, University of CalgaryUniversity of Calgary
KeywordsMaasaiPhotovoiceGeographySocioeconomicsSociologyEconomic growth

Abstract

fetched live from OpenAlex

The Maasai pastoralists in sub-Saharan Africa depend on their livestock for income and food. Livestock production can be significantly improved by addressing animal health concerns. We explored the use of photovoice, a participatory action research method, to strengthen our understanding of the Maasai's animal health needs. Nine interviewees, representing warriors, elders, and women, identified animal, social, and human health themes. The use of photography provided a new medium for Maasai to express their needs and a focus for researcher-participant communications, thereby facilitating new insights across language and cultural barriers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.773
GPT teacher head0.609
Teacher spread0.164 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreReview

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

Citations4
Published2015
Admission routes2
Has abstractyes

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