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Record W4414652344 · doi:10.3390/ani15192876

Dog Guardian Interpretation of Familiar Dog Aggression Questions in the C-BARQ: Do We Need to Redefine “Familiar”?

2025· article· en· W4414652344 on OpenAlexafffund
Sarita D. Pellowe, Carolyn J. Walsh

Bibliographic record

VenueAnimals · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsVancouver Coastal HealthMemorial University of Newfoundland
FundersMemorial University of NewfoundlandMitacs
KeywordsSingletonAggressionInterpretation (philosophy)GuardianLegal guardianSuicide prevention

Abstract

fetched live from OpenAlex

The C-BARQ familiar dog aggression (FDA) subscale contains four items relating to threatening responses towards familiar dogs in the same household (i.e., dog rivalry). In a recent study, we noticed that 92 of 157 guardians who owned only one dog completed the FDA items, generating an unexpected score. We followed up with participants to explore whether lifestyle factors influenced their completion of the FDA items. Singleton dogs with FDA scores were more likely to regularly participate in social activities with other dogs, with many scores based on such interactions with non-household dogs. The singleton dogs with FDA scores also had marginally lower fear-related C-BARQ scores compared to singletons with no FDA score and dogs living in multi-dog households. We then conducted a scoping review of articles using English versions of the C-BARQ and found wide variation in whether or not FDA scores were reported. Studies that reported significant FDA findings often did not indicate the proportion of scores in their data that came from singleton dogs, raising issues of accuracy and interpretation of the subscale. We discuss ways to clarify the interpretation of the FDA questions by dog guardians and hope to promote further consideration of practices to improve replicability across studies.

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)0.000
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 score0.566
Threshold uncertainty score0.440

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 head0.013
GPT teacher head0.359
Teacher spread0.346 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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