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Record W7024248379

Public Perceptions of Dangerous Dogs and Dog Risk

2023· book· en· W7024248379 on OpenAlexaboutno aff

Bibliographic record

VenueEdge Hill University Research Information Repository (Edge Hill University) · 2023
Typebook
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDog biteLegislationSituational ethicsRisk perceptionQuarter (Canadian coin)PerceptionPublic involvementQuestionnaire
DOInot available

Abstract

fetched live from OpenAlex

This report presents a background literature survey and the results of research undertaken to gain insights into public perceptions of dangerous dogs and dog risk in the UK. The project used a public questionnaire distributed primarily via closed social media groups and analysed the responses from 1,535 UK participants. Most of the questionnaire respondents (88.6%) were current dog owners. Of these, around a quarter were first-time dog owners and one fifth of all respondents had experience of bull breeds. A clear majority (87.1%) of respondents said that dogs liked them and that they were ‘good with dogs’. Of particular interest to the team were questions of where the public get their information about dog behaviour and dog risk, what is understood as ‘dangerous’ dog behaviour, people’s understanding of canine body language, and situational awareness of bite risk. The aim of this research is to contribute to finding an alternative strategy to breed specific legislation which protects the public and dogs.

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)0.003
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.043
GPT teacher head0.296
Teacher spread0.252 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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