Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".