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Record W3191425605 · doi:10.6000/1929-4409.2021.10.145

Women and Economic Production: Towards Sustainable Livelihoods in Zimbabwe

2021· article· en· W3191425605 on OpenAlex
Tafadzwa Clementine Maramura, D.R. Thakhathi, Happy Mathew Tirivangasi

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican studies and sociopolitical issues
Canadian institutionsnot available
Fundersnot available
KeywordsLivelihoodFirewoodBusinessAgricultureProduction (economics)SocioeconomicsEconomic growthCapital (architecture)GeographyEconomics

Abstract

fetched live from OpenAlex

Persistent drought and economic collapse in Zimbabwe have seen most, if not all, rural women shifting from the receiving end to the giving end. Rural women have since initiated several livelihood activities to make ends meet, as they are the most vulnerable whenever they are left to look after children at home. The paper aims to examine rural livelihoods and how they contribute to economic production in Ward 5 of Bikita district. A mixed design in the form of a case study was employed in this study. Systematic random sampling was used to select 40 households, which provided data for the study out of 409 households in Ward 5. The study used questionnaires and semi-structured interviews as data collecting instruments. Several livelihood activities were noted in the ward including seasonal farming, gardening, community-based and money lending and saving schemes (fushai), informal trading, and petty trading as selling thatch grass and firewood, among others. However, climate change and drought, economic crisis, lack of capital and poor soils and poor farming methods were some of the constraints faced in rural livelihoods. The paper concludes with several recommendations for eradicating rural livelihood challenges.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score0.435

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.047
GPT teacher head0.353
Teacher spread0.306 · 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