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Record W3161073046

Private Governance and Animal Welfare

2018· article· en· W3161073046 on OpenAlexaboutno aff
Sarah J. Morath

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal welfareWelfareBusinessCorporate governancePublic economicsProcurementEconomicsMarket economyMarketingFinanceBiology
DOInot available

Abstract

fetched live from OpenAlex

At present, there are no animal welfare standards for agricultural animals at the federal level. Yet public support for agricultural animal welfare is growing. Private entities such as retailers have responded in a number of ways including adopting voluntary animal welfare standards and adding animal welfare labels to products. For example, Walmart recently announced it would adopt the globally recognized “five freedoms” animal welfare and McDonald’s, the biggest purchaser of eggs in Canada and U.S., announced plans to go “cage free” over the next ten years. This article explores the various types of private governance that has emerged in response to both a regulatory void and growing consumer demand for the humane treatment of agricultural animals. Part I of this article provides a brief overview of farmed animal welfare concerns. Part II introduces private environmental governance and the regulatory instruments available to private entities. This part focuses on three instruments used to address farmed animal welfare: performance standards; information; and procurement or supply chain contracting. Part IV explores different motivations for the changes being made by private entities including consumer preference, state requirements, and investor concerns. The article concludes by offering some standards by which the effectiveness of private animal welfare governance could be measured.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.713

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.198
Teacher spread0.188 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2018
Admission routes1
Has abstractyes

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