Responses to Risk – The Role of Policy and Compensation Schemes Les réponses au risque – Le rôle des politiques et des systèmes d’indemnisation Reaktionen auf das Risiko – die Rolle von Politik und Entschädigungsprogrammen
Bibliographic record
Abstract
summary Responses to Risk – The Role of Policy and Compensation Schemes Livestock diseases can spread quickly and cause very high losses, which has prompted public policy response, including the use of compensation. There is no generic and optimal system of compensation, but the scope and modalities of these policies and the implications for public expenditure and private incentives deserve public, economic and scientific scrutiny. The rationale for government intervention is to internalise the externalities of prevention and control activities. But ‘more is not always better’ and the types of measures employed and the total cost, matter. This article provides some insights from five countries based on a recent OECD report. Compensation schemes can be purely public, such as in Canada, or based on a private–public scheme with some formal representation of the industry to facilitate the engagement of the sector. The costs covered by the compensation schemes included the full market value of destroyed animals in most countries. Cost‐sharing arrangements are an important component of compensation schemes in Australia, Germany and the Netherlands. Expenditure on compensation can be extremely onerous and needs to be disciplined, while at the same time, engaging stakeholders. International comparisons of experiences and knowledge about the costs and benefits of past outbreaks can be very helpful in designing more efficient policy responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".