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Record W4293224812 · doi:10.1080/00220388.2022.2113065

Hiding or Pleasing: Spousal Disagreement Among Ugandan Maize Farmers

2022· article· en· W4293224812 on OpenAlex

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.

fundA Canadian funder is recorded on the work.
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

VenueThe Journal of Development Studies · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
FundersConsortium of International Agricultural Research CentersInternational Development Research CentreInternational Fine Particle Research InstituteUnited States Agency for International Development
KeywordsAsset (computer security)Test (biology)EconomicsField (mathematics)Information asymmetrySocial psychologyDemographic economicsPsychologyMicroeconomicsComputer science

Abstract

fetched live from OpenAlex

To gain a better understanding of intrahousehold bargaining processes, surveys increasingly collect data from co-heads individually, especially on decision-making, asset ownership and labour contributions. However, answers provided by co-heads to the same set of questions often differ substantially. Recent research suggests that while some of this disagreement is due to random measurement error and cognitive bias, part also reflects non-overlapping information sets. We document differences in answers between male and female co-heads in monogamous smallholder maize-farming households in Uganda. We first confirm that not all disagreement can be explained by measurement error or bias. Using a field experiment, we then test if disagreement is due to information asymmetry between male and female co-heads. We also test an alternative explanation where discord is attributed to co-heads’ tendency to respond in line with prevailing gender norms and social customs. While the interventions did seem to reduce discord in survey response about decision-making, we do not find that information asymmetry nor reporting in line with gender norms and customs are the primary drivers of disagreement.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.293
Teacher spread0.192 · 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