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Record W3043290570 · doi:10.22215/etd/2015-10624

The Gendered Implications of the Expansion in Commercial Sugarcane Production: A Case Study of Contract Farming in Magobbo, Zambia

2015· dissertation· en· W3043290570 on OpenAlexaff
Vera Rocca

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsCarleton University
Fundersnot available
KeywordsDisadvantagedAgricultureProduction (economics)EstateScale (ratio)InequalityEconomic growthContract farmingEconomicsBusinessLabour economicsGeographyFinance

Abstract

fetched live from OpenAlex

This paper presents evidence on the gender differentiated effects in the nucleus-estate outgrower arrangement from a case study of a sugarcane outgrower scheme in the community of Magobbo, Zambia.Specifically, the paper explores women's participation in the scheme, access to employment, decision-making, control of household income, and access to natural resources.Women are disadvantaged in these areas overall, though there is a key generational difference.As well, both women and men enjoy increased economic stability and improvements in family diets.I find that the outcomes observed are influenced by: 1) the existing inequalities in access to land and discriminatory gender norms; 2) the institutional arrangements of the outgrower model; and 3) the gendered division of labour.These findings contribute a nuanced discussion of the gender differentiated effects of agricultural investments to the literature on women in contract farming and large-scale land acquisitions for agriculture.Ruth Hall for welcoming me into the Futures Agricultures Research Consortium to collaborate with their multi-country study, Land and Agricultural Commercialization in Africa (LACA).Being able to participate in this network was immensely valuable and I'm grateful for the opportunity to participate in research planning and share tentative findings and analysis in South Africa.Thanks also to The University of Zambia for granting me a research permit and Chrispin Matenga for the initial days spent working in collaboration at Mazabuka and for our discussions in Ghana and South Africa.I extend my gratitude to my translator as well, Chimuka Nsanje, especially for her tact in locating participants in the scheme.Finally, for offering guidance on the design of this study and comments on earlier versions, I thank Dr. Jean Daudelin as my thesis supervisor.Any errors that remain are my own.

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)0.002
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.360

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.291
Teacher spread0.249 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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