Healthy publics as multi-species matters: solidarity with people’s pets in One Health promotion
Bibliographic record
Abstract
Abstract Climate change is contributing to local disasters, and pets increasingly figure in mediated views and responses. By theorizing such responses, we expand on the conceptualization of “healthy publics”. In our view, healthy publics can arise from multi-species entanglements, out of which enactments of solidarity may emerge. Such enactments may encompass people with pets, as well as the pets themselves. Such enactments are selective, however, because they highlight certain lives and vulnerable situations while obscuring others. To develop this line of inquiry, we treated a major flood that took place in 2013 as a case-study. Participant-observation, social media, and qualitative interviews informed our analysis. During the immediate responses to the flood, a particular human-animal dyad became emblematic of people helping one another and their pets. As the floodwaters subsided, media reports helped to coordinate a public response to shelter people and pets on a temporary basis. Yet in the months following the flood, housing insecurity worsened for people with pets. With the passage of time, media coverage became instrumental in resolving housing crises for people with pets, but only on a case-by-case basis. Housing security for people with pets, as a policy issue, remains disconnected from planning to improve resilience overall and to enhance preparedness for disasters. Our analysis highlights the value of engaged research in foregrounding policy issues that influence the lives of people and pets. We conclude that, to be healthy, multi-species publics must entertain questions about whose lives come to matter most. The relative health of a public pivots on the extent to which policies emphasize inclusion and equity. By extension, some publics qualify as unhealthy, which could seem like a provocative claim. At this historical juncture, we feel compelled to defend decision-making process that attend not only to differences of opinion, but also to differences in possible ways of being in the world.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".