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Record W3160673617 · doi:10.1596/1813-9450-9663

Polygyny and Farm Households' Resilience to Climate Shocks

2021· book· en· W3160673617 on OpenAlexaff
Sylvain Dessy, Luca Tiberti, Marco Tiberti, David Zoundi

Bibliographic record

VenueWorld Bank, Washington, DC eBooks · 2021
Typebook
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsResilience (materials science)PolygynyClimate resilienceGeographyClimate changeSociologyGeologyOceanographyDemographyMaterials science

Abstract

fetched live from OpenAlex

Climate change and weather shocks pose
\n major challenges for household income security and
\n well-being, especially for smallholder farmers’ communities.
\n In such communities, imperfect risk insurance and labor
\n markets may induce households to use traditional
\n institutions such as polygyny to harness their size and
\n composition to their resilience strategies against these
\n shocks. This paper tests this hypothesis by analyzing how
\n polygyny’s interaction with droughts affects crop yields.
\n For identification, the paper relies on the spatial
\n variation in polygyny’s prevalence across Mali’s rural
\n communes and the randomness of drought episodes. The
\n findings show that polygynous communities are more resilient
\n to drought-induced crop failure. Exploration of the
\n mechanisms shows that polygynous communities diversify their
\n income sources more than monogamous ones, including via
\n child marriage—a phenomenon known to undermine women’s
\n outcomes. As the literature links polygyny to
\n underdevelopment, interventions to eliminate it should make
\n formal resilience and adaptation strategies available to
\n drought-prone communities. Failure to do so may entrench
\n political opposition to enforcing a ban on polygyny and
\n child marriage.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.811
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.213
Teacher spread0.201 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations15
Published2021
Admission routes1
Has abstractyes

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