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Record W2783076493 · doi:10.5604/01.3001.0010.7921

ANIMAL WELFARE AS A PUBLIC GOOD IN POLISH OPINION

2018· article· en· W2783076493 on OpenAlexaboutno aff
Sylwia Małażewska, Edyta Gajos

Bibliographic record

VenueAnnals of the Polish Association of Agricultural and Agribusiness Economists · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal welfareWelfarePublic economicsAgriculturePublic opinionPublic goodQuarter (Canadian coin)EconomicsSample (material)Public interestBusinessDemographic economicsPolitical scienceMarket economyGeographyPoliticsBiologyMicroeconomicsLawEcology

Abstract

fetched live from OpenAlex

The topic of public goods in agriculture has become a very important and widely discussed subject in recent years. The demand for public goods generated by agriculture results from the expectations of the whole society, and the goods themselves are considered as a part of public health. Accordingly, in this study an attempt was made to determine the importance of animal welfare, as an example of the public goods generated by agriculture, to the Polish citizens and its determinants. Empirical studies were conducted in the second quarter of 2017 on a representative sample of 500 Polish citizens. It was found that people in Poland generally rate animal welfare very highly (average score 88.43 on a scale of 0-100) and that there is a statistical difference in the assessment of the importance of animal welfare on grounds of interest in healthy food, the number of people in the household and sensitivity to the beauty of nature. Additionally, factors determining the importance of animal welfare include: consuming organic food, interest in culture, interest in fashion, sex, age and smoking.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0040.003
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.235
Teacher spread0.167 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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Same venueAnnals of the Polish Association of Agricultural and Agribusiness EconomistsSame topicEconomic and Environmental ValuationFrench-language works237,207