One Health promotion and the politics of dog management in remote, northern Australian communities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".