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Record W4210929362 · doi:10.3389/fvets.2021.808767

“Cattle Welfare Is Basically Human Welfare”: Workers' Perceptions of ‘Animal Welfare' on Two Dairies in China

2022· article· en· W4210929362 on OpenAlexaff
Maria Chen, Daniel M. Weary

Bibliographic record

VenueFrontiers in Veterinary Science · 2022
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersGood Ventures FoundationOpen Philanthropy Project
KeywordsAnimal welfareWelfareBusinessPublic economicsPolitical scienceEconomicsBiologyEcologyLaw

Abstract

fetched live from OpenAlex

‘Animal welfare' (动物福利) is a foreign term in China, and stakeholder interpretations can affect receptiveness to the concept. Our aim was to explore workers' perceptions of animal welfare on two dairies in China. We used a mini-ethnographic case study design, with the first author (MC) living for 38 days on one farm and 23 days on a second farm. MC conducted semi-structured interviews ( n = 13) and participant observations ( n = 41) with farm management and staff. We used template analysis to generate key themes from the ethnographic data. Responses revealed a connection between human and animal welfare. Workers saw human welfare as a prerequisite to animal welfare, and cattle welfare as potentially mutually beneficial to humans. Some workers also saw an ethical obligation toward providing cattle with good welfare. Though some workers were unfamiliar with the term ‘animal welfare,' in daily practice caring for cattle led farm workers to ponder, prioritize, and make decisions relevant to welfare including lameness, morbidity, and nutrition. Workers in management positions appeared to embrace evidence-based animal care improvements, especially those which were perceived to also benefit people. Based on our findings, we suggest animal welfare initiatives should (1) consider worker welfare, (2) clearly communicate the concept of ‘animal welfare,' (3) identify mutual benefits, and (4) provide pragmatic, evidence-based strategies to improve welfare.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.032
GPT teacher head0.333
Teacher spread0.302 · 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 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

Citations8
Published2022
Admission routes1
Has abstractyes

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