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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 OpenAlex
Frank van der Meer, Eoin Clancy, Adam Thomas, Susan Kutz, Jennifer Hatfield, Karin Orsel

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.049
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.953
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0490.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.007
Science and technology studies0.0020.012
Scholarly communication0.0010.001
Open science0.0040.001
Research integrity0.0000.001
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 head0.773
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