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Record W2528442096 · doi:10.1163/15685306-12341425

Taking Care of Companion Animals

2016· article· en· W2528442096 on OpenAlex
Mark C. J. Stoddart, Liam Swiss, Nicole Gerarda Power, Lawrence Felt

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSociety and Animals · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAnimal welfareWelfareLocal governmentPolitical scienceGovernment (linguistics)Position (finance)Public administrationCompanion animalBusinessPublic economicsPublic relationsLawEconomicsVeterinary medicineMedicineBiologyFinance

Abstract

fetched live from OpenAlex

Focusing on local government and non-governmental nonhuman animal welfare organizations, this paper reports survey results on institutional policies, interpretive frameworks, and practices regarding companion animals in Newfoundland and Labrador, Canada. The findings suggested that local governments and animal shelters use different interpretive frameworks of companion animal welfare, with the former taking a human-centric position and the latter focusing on animal well-being. The results showed that most local governments are not well engaged with animal welfare issues. Instead, these issues are more often dealt with by non-governmental organizations that operate on limited budgets and rely heavily on volunteer labor. Whereas federal and provincial governments are responsible for legislating companion animal welfare, practical implementation of animal welfare has been largely the responsibility of non-governmental organizations. Our findings demonstrated that the ways that animal welfare policy is interpreted and enacted at the local level have significant implications for animal well-being more broadly.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.024
GPT teacher head0.338
Teacher spread0.313 · 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