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Record W4377104265 · doi:10.3390/socsci12050306

Gendering the Political Economy of Smallholder Agriculture: A Scoping Review

2023· review· en· W4377104265 on OpenAlexafffund
Madelyn Clark, Shashika Bandara, Stella Aguinaga Bialous, Kathleen Rice, Raphael Lencucha

Bibliographic record

VenueSocial Sciences · 2023
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsAgriculturePoliticsUnit (ring theory)Variety (cybernetics)Gender analysisPolitical economySociologyPolitical scienceEconomicsEconomic growthPsychologyGeography

Abstract

fetched live from OpenAlex

Gender plays a prominent role in shaping the practices and experiences of smallholding farming households. This scoping review seeks to chart and analyze how gender is used in the existing literature on the political economy of smallholder agriculture. The aim of this review is to first identify the extent to which gender is addressed as a unit of analysis in this body of literature, and second, to identify when and how gender is incorporated in this body of literature. The limited work on this topic may be due to a variety of factors, the most notable of which is the failure of political economy literature to attend to the small scale and the limited attention paid to the social dynamics of women and men in farming households. Classical political economy frameworks tend to dismiss micro-processes and trends in favor of macro-structural conditions. Included articles approach gender in two distinct ways: empirical (which frames gender as a binary unit of analysis, i.e., man–woman) and analytic (a construction that operates in different ways in different contexts). This review provides a nuanced understanding of how gendered identities produce and are produced by political economy, and how political economy shapes and is shaped by gender and household dynamics.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.886
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.203
GPT teacher head0.380
Teacher spread0.177 · 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 designOther design
Domainnot available
GenreReview

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

Citations6
Published2023
Admission routes2
Has abstractyes

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