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Record W2097014570 · doi:10.18174/51717

Milking drylands : gender networks, pastoral markets and food security in stateless Somalia

2010· dissertation· en· W2097014570 on OpenAlexfundno aff
Michele Nori

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
FundersInstitut Alam Sekitar dan Pembangunan, Universiti Kebangsaan MalaysiaUniversité de NamurUniversiteit van AmsterdamUniversity of SussexEuropean CommissionUniversity of OxfordOverseas Development InstituteUniversity of TorontoInternational Fine Particle Research Institute
KeywordsSomaliFood securityCommoditizationLivelihoodCorporate governanceOrder (exchange)BusinessRelevance (law)Development economicsPolitical scienceAgricultureEconomicsGeographyMarket economy

Abstract

fetched live from OpenAlex

The Milking Drylands research initiative addresses the critical issues of food security, market integration, gender roles and governance matters in a peculiar area of the world, the Somali ecosystem. The research aims at exploring interesting dynamics of ongoing social change, in order to stimulate appropriate understanding of complex pastoral economics and provide options for sensitive interventions. More specifically camel milk marketing is a developing women enterprise in Somali drylands, aimed at ensuring food security, generating some income and providing a buffer to cope with critical situations. Within a livelihood perspective socio-economic processes related to camel milk commoditization are investigated, in order to assess the relevance of existing embedding institutions on the construction of pastoral markets, with a special concern for the relevance of gender roles, state control and governance.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.215
Teacher spread0.209 · 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
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

Citations24
Published2010
Admission routes1
Has abstractyes

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