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Record W4411226904 · doi:10.1080/10888705.2025.2515856

The Value of a Statistical Life of a Cat: Owner Demographics and Management Practices Impacting Willingness to Pay for Welfare Measures

2025· article· en· W4411226904 on OpenAlexaffabout
Denise King, Panagiotis Tsigaris

Bibliographic record

VenueJournal of Applied Animal Welfare Science · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsDemographicsWelfareWillingness to payValue (mathematics)BusinessActuarial sciencePublic economicsDemographic economicsEconomicsStatisticsDemographyMathematicsMicroeconomicsSociologyMarket economy

Abstract

fetched live from OpenAlex

This study estimates the Value of a Statistical Life of a cat (VSLC) in Kamloops, British Columbia, to inform animal welfare policies and community-level interventions. Using a contingent valuation survey, we assessed cat owners' willingness to pay (WTP) for control measures aimed at reducing the risk of premature death among outdoor cats. We compared WTP between indoor and outdoor cat owners and examined how demographic and attitudinal factors influenced WTP. The mean VSLC was approximately $8,000 among those willing to pay, and about $4,000 when non-payers were included. Notably, VSLC estimates were similar for both indoor and outdoor cat owners, suggesting that WTP reflects broader community concern for cat welfare rather than individual pet ownership circumstances. Regression results indicated that higher WTP was significantly associated with female gender, household income, concern for local wildlife, and support for cat licensing. These findings provide an economic basis for policies promoting responsible pet ownership and risk reduction for outdoor cats.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.047
GPT teacher head0.276
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes2
Has abstractyes

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