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Record W4299879846 · doi:10.1111/ajae.12349

Upside risk, consumption value, and market returns to food safety

2022· article· en· W4299879846 on OpenAlex
Vivian Hoffmann, Sarah Kariuki, Janneke Pieters, Mark Treurniet

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Agricultural Economics · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsCarleton University
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekConsortium of International Agricultural Research CentersAgricultural Technology Adoption Initiative
KeywordsFood safetyInvestment (military)Consumption (sociology)Production (economics)BusinessIncentiveRisk premiumMarginal valueEconomicsMicroeconomicsMonetary economics

Abstract

fetched live from OpenAlex

Abstract We investigate the effect of a modest food safety premium on semisubsistence farmers' investment in a food safety technology. We demonstrate theoretically that in the face of production uncertainty, a market incentive below the marginal production cost of achieving the safety standard can increase food safety investment among farmers motivated by private health returns. We test this prediction through a randomized controlled trial in Kenya through which members of existing farmer groups were offered an opportunity to purchase a food safety input, and half were offered a 5% market premium for produce that met the associated regulatory standard. Access to the premium more than doubled investment in the food safety technology. In line with the model's prediction, most premium‐induced adoption was by farmers motivated by a combination of health and financial rewards.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.466

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 studies0.0010.000
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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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