Voice for the Voiceless: Amplifying Animal Issues in Disaster Management and Media
Bibliographic record
Abstract
How do the media portray companion animals, commonly known as pets, and their guardians during natural disasters? This study explores the crucial role media has played in shaping the public’s understanding of animal-related issues during the wildfires that swept through Nova Scotia, Canada, in May and June 2023. This case study examines how various platforms—from Twitter to government websites and local news outlets—covered the challenges faced by animals and their guardians during this crisis. By analyzing a wide range of sources, the study uncovers practical examples of how people interacted with animals during the wildfires. These interactions include companion animal guardians caring for their pets, farmers protecting their livestock, and efforts to safeguard local wildlife. The research reveals how these human–animal bonds contributed to mutual resilience in the face of disaster. To date, there are currently no standard guidelines for media coverage of animals affected by disasters. This study fills that gap, offering valuable insights into the often overlooked area of human–animal relationships during wildfires, i.e., a specialized but important aspect of disaster research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".