Caring During Crisis: Animal Welfare During Pandemics and Natural Disasters
Bibliographic record
Abstract
From April 29 to May 1, 2007, the University of Guelph hosted a symposium, Caring During Crisis: Animal Welfare During Pandemics and Natural Disasters, with the objectives (a) of raising awareness about how nonhuman animals and the people who care for them are affected during emergencies and (b) of sharing knowledge about how animal welfare may be addressed during these situations. The symposium attracted 150 participants, representing 71 organizations from across Canada, the United States, the United Kingdom, Australia, Chile, and the Cayman Islands. The audience also brought a range of perspectives to the issues - from individuals representing animal protection and commodity organizations to municipal government officials responsible for community safety and correctional services; many of these individuals had little or no animal experience. To take advantage of this diverse audience and range of interests, the symposium was structured with formal presentations by internationally recognized experts, followed by panel discussions at the end of each session to facilitate contributions by the audience. At the conclusion of the 3 days, it was clear that our emotional, economic, and ecological relationships with animals require thoughtful integration of animal care within formal policy and planning for emergency response.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".