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Record W2156317452 · doi:10.3382/ps.2010-0138

Values and public acceptability dimensions of sustainable egg production

2011· article· en· W2156317452 on OpenAlexaff
Paul Β. Thompson, M. C. Appleby, L. Busch, Linda Kalof, Mara Miele, Bailey Norwood, Edmond A. Pajor

Bibliographic record

VenuePoultry Science · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsProduction (economics)OddsSustainabilityWelfareSustainable productionAnimal welfareMarketingPublic economicsConsumption (sociology)Focus groupBusinessPoliticsLogistic regressionEconomicsMicroeconomicsPolitical scienceBiologySociologyMedicineEcologySocial science

Abstract

fetched live from OpenAlex

The attributes of egg production that elicit values-based responses include the price and availability of eggs, environmental impacts, food safety or health concerns, and animal welfare. Different social groups have distinct interests regarding the sustainability of egg production that reflect these diverse values. Current scientifically based knowledge about how values and attitudes in these groups can be characterized is uneven and must be derived from studies conducted at varying times and using incomplete study methods. In general, some producer and consumer interests are translated through markets and are mediated by market mechanisms, whereas others are poorly reflected by economic behavior. An array of survey and focus group research has been performed to elicit consumer and activist beliefs about performance goals they would expect from an egg production system. These studies provide evidence that consumers' market behavior may be at odds with their ethical and political beliefs about performance goals.

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)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
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 head0.022
GPT teacher head0.214
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

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 designObservational
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

Citations31
Published2011
Admission routes1
Has abstractyes

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