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Record W3044570426 · doi:10.1038/s41598-020-69316-0

One Health promotion and the politics of dog management in remote, northern Australian communities

2020· article· en· W3044570426 on OpenAlexaff
Victoria Brookes, Michael P. Ward, Melanie Rock, Chris Degeling

Bibliographic record

VenueScientific Reports · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Calgary
FundersNational Health and Medical Research CouncilNational Medical Research CouncilMedical Research Council
KeywordsPoliticsPromotion (chess)Health promotionGeographyMedicinePolitical sciencePublic healthNursing

Abstract

fetched live from OpenAlex

Community perspectives are rarely sought or integrated into dog management policy and practice. Dog management in remote communities in Australia has focused on reducing the number of dogs, which is often implemented by visiting veterinarians, despite widely-held opinions that fly-in-fly-out services provide only temporary solutions. We conducted participatory research in a group of remote communities in northern Australia to explore how dog-related problems arise and are managed, and explain their impacts from a One Health perspective. Over the course of a year, 53 residents from a range of backgrounds contributed through in-depth interviews with key community service providers, and informal semi-structured discussions with community residents. Free-roaming dogs have broader impacts on canine and human health than previously documented. Dog-keeping norms that enable free-roaming can enhance human and dog wellbeing and intra-family connectivity. This can also cause disengagement and conflict with other residents, leading to resentment and occasionally violence towards dogs. Dog-related problems are underpinned by constraints associated with remote-living, governance and differing sociocultural norms. Focusing on dog population reduction detracts from the welfare benefits and sociocultural value of free-roaming dogs and undermines community-determined management that can overcome constraints to support local values and co-promote canine and human wellbeing.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.056
GPT teacher head0.349
Teacher spread0.293 · 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 designNot applicable
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

Citations31
Published2020
Admission routes1
Has abstractyes

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