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Record W7044112423

What is farm animal welfare?

2020· other· en· W7044112423 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEpsilon Archive for Student Projects (University of Southampton) · 2020
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMeaning (existential)ReferentMainstreamLatin AmericansPerceptionAnimal welfareMetaphorSet (abstract data type)Focus groupConsumer behaviour
DOInot available

Abstract

fetched live from OpenAlex

Authors stress that farm animal welfare (FAW) has become a mainstream contemporary societal demand worldwide, resulting in research conducted with FAW. The most popular type of research are surveys that analyse consumers’ attitudes towards FAW, yet, these are limited geographically to the European Union, the United States, and Canada. Very few studies have been done in Latin America, regardless of evidence that suggests an expected increase in the social demand of FAW and its associated products. FAW related knowledge in terms of consumer preferences today, still scarce in Latin American countries, with only Mexico, Chile, and Brazil being the referent countries creating scientific publications that address FAW. Nevertheless, such scientific publications often focus on farmers and slaughter practices, excluding consumers’ attitudes and perceptions. Thus, this study acknowledges that the agri-food chain is integrated by different actors, focusing on understanding what FAW is from the consumers’ perception perspective.
\nThis study aimed to investigate the Mexican respondents’ perceptions in their role of consumers of animal-based food when forming a meaning for FAW. Thus, a novel approach was embraced by applying the Zaltman Metaphor Elicitation Technique (ZMET) and interpreting the results based on the Means-End Chain (MEC) theory and the Schwartz’s personal values theory; this approach, together with the findings, are the study’s key contribution. The findings in this research suggest that when attaching a meaning for FAW, the meaning respondents build is complex, being integrated by a set of hierarchical relationships. These relationships are integrated by elements like attributes leading to consequences, to achieve a specific set of values. The study displays them graphically through a Hierarchical Value Map (HVM) representing the first-ever Mexican respondents’ mental model when forming a meaning for FAW.
\nBy examining such elements, this study discovered that respondents consistently reflected FAW as a set of specific and distinctive characteristics in animal-based food; such characteristics are the attributes free from chemicals, more natural, higher quality, cruelty-free, better taste, ethical and artisan-made. Also, the respondents perceived FAW as a physiological or psychological result happening not to them as a person, but to the farmed animals, taking the form of a set of consequences that were consistently evoked by them and that reflect their thoughts of FAW being no pain/painless life, freedom of movement, free from stress, non-alteration of the animals’ development, access indoor/outdoor, access to natural food and water, no overexploitation, dignified life, access to medical care, non-forced reproduction, access to socializing with their own species, access to rest and sleep, dignified slaughter and recognition of farmed animals as sentient beings the recurrent constructs. Finally, when thinking of FAW, the respondents ultimately reach three end-states: being compassionate, wellness, and achievement.
\nThe results displayed here might serve as a source of useful knowledge or a guideline when the time comes, and the actors in the agri-food chain -producers, distributors, marketers, and policy-makers- in Mexico decide to listen to the consumer concerns by embracing FAW practices and designing FAW frameworks which goal is the insurability of farm.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.062
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.023
GPT teacher head0.263
Teacher spread0.240 · 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