MétaCan
Menu
Back to cohort
Record W4397039188 · doi:10.21275/sr23625065717

Women's Access to and Control over the 5 Forms of Capital in Machakos Town Sub-County, Kenya

2023· article· en· W4397039188 on OpenAlexfundno aff
Angela Adhiambo Opondoh Geoffrey

Bibliographic record

VenueInternational Journal of Science and Research (IJSR) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
FundersInternational Development Research CentreGlobal Affairs CanadaBill and Melinda Gates Foundation
KeywordsCapital (architecture)GeographySocioeconomicsEconomic growthPolitical scienceBusinessEconomicsArchaeology

Abstract

fetched live from OpenAlex

This mixed-methods study examines female small livestock owners' five forms of capital (personal, human, social, financial, and physical) in Kola and Kalama wards of Machakos Town sub-county, Kenya. Based on analysis of 39 individual interviews and 3 focus group discussions we analyze how the complex and dynamic position of smallholder women farmers at the household and community level is influenced by local culture and politics. Gender intersects with ethnicity, age, socio-economic status, education, and marital status to create patterns of disadvantage and marginalization from resources including decision-making. Men use all forms of violence to control most forms of capital. The only forms of capital which women have some control over are physical capital (small livestock such as chickens, goats) and social capital (groups, networks).Strategically, these women have developed "code of conduct" and that uses their strong social capital to achieve their goals. Women's groups provide opportunities for women to increase their power in their households and in their communities and social networks can be targeted for interventional research aimed at increasing access to livestock vaccines and veterinary services.

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)0.011
metaresearch head score (Gemma)0.003
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 score0.448
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
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 head0.053
GPT teacher head0.438
Teacher spread0.386 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Science and Research (IJSR)Same topicLegal Issues in South AfricaFrench-language works237,207