Bibliographic record
Abstract
The twenty-first century is characterised by 'epidemiological globalisation' on an unprecedented scale with resulting impacts at the interface of economic, scientific, social and political forces arising from the emergence and re-emergence of animal diseases. Throughout history, animals have served as a source to humankind of food, transportation, medicines, entertainment, clothing, fuel, military advantage and financial security. It is therefore not at all surprising that animal diseases have resulted in significant social and political impacts that have shaped and continue to shape the course of national and international events. The social impacts can be expressed as indirect health consequences or behavioural changes, changes in societal values and changes in social standing and can be felt at the individual, family or community level. The political impact of major disease outbreaks can include loss of public and consumer confidence, resistance to investments in disease surveillance, reluctance to report disease detections in a timely or transparent manner, failure to implement science-based international standards for safe trade (which protect animal, human and ecosystem health) and the removal of government officials. The magnitude of these impacts would support that social and political impacts warrant their inclusion in the consequence assessment of a robust animal disease risk analysis framework.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".