One Health and reconciliation: media portrayals of dogs and Indigenous communities in Canada
Bibliographic record
Abstract
This qualitative media analysis explores how the Canadian Broadcasting Corporation (CBC) portrayed 'dog problems' and their solutions in Indigenous communities in Canada from 2008 through 2018. We apply a One Health framework to demonstrate how human, animal, and the socio-environmental health are interconnected, which aligns more explicitly with Indigenous worldviews. Through this analysis, we respond to the Truth and Reconciliation Commission of Canada (TRC) Calls to Action, specifically Action 19 (health inequity) and Action 84 (media). We found that the CBC portrayed dogs as "strays" and focused mainly on the removal of dogs, whether rehoming by animal rescue groups or through culling, and that rescue groups were portrayed as 'animal lovers'. Meanwhile, journalists sometimes mentioned the lack of policies to support community-driven dog population control and veterinary services, but these policy deficits did not receive emphasis. The CBC coverage did not highlight systemic injustices that can impact dog health and welfare in Indigenous communities. This media analysis outlines ways forward for reconciliation with Indigenous communities when the media reports on dogs; we recommend journalists (i) focus on lack of veterinary services in communities and the impacts rather than the removal of dogs, (ii) discuss broader systemic structures and policies that limit access to veterinary services in Indigenous communities and (iii) how such resource constraints impact human and animal health.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".