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Record W4293124873 · doi:10.6000/2371-1647.2022.08.01

The Human Perspective in Consumer Ethics and Animal Welfare Issues: Envisioning a Future for Change

2022· article· en· W4293124873 on OpenAlexvenueno aff
Alessandro Bonadonna, Luigi Bollani, Giovanni Peira

Bibliographic record

VenueJournal of Advances in Management Sciences & Information Systems · 2022
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal welfareEuropean unionPopulationPublic economicsBusinessWelfareTreatyPolitical scienceEconomic policyEconomicsLawMedicineBiologyEnvironmental health

Abstract

fetched live from OpenAlex

Animal welfare has been a subject of interest for the European Union since the 1970s, with the definition of animal protection guidelines during international transport, on farms and for slaughter. However, the Legislator’s concern found its highest expression in the Animal Welfare Protocol of the Treaty of Amsterdam, where animals are defined as sentient beings, therefore worthy of attention in the policies developed by the European Union and its Member States. Nowadays, the interpretation of the animal welfare concept as an element that contributes to increasing profitability is also integrated by respect for the animal’s feelings and, consequently, the related different biological manifestations. Food scandals and diseases, on the one hand, and the emergence of a new approach to consumer ethics, on the other, have also strongly sensitized the European population about the importance of protecting animal welfare. Based on the above considerations, this study provides a framework to understand whether animal welfare should merely be considered as a product of EU strategies dedicated to the economic and competitive performance of agricultural and agro-industrial enterprises or whether it can also be assessed as a useful tool to minimize the environmental impact, through breeding practices and food habits, and therefore encourage more sustainable development.

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)0.004
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.002
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 head0.075
GPT teacher head0.417
Teacher spread0.342 · 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.

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
Published2022
Admission routes1
Has abstractyes

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