Policy adaptability in practice
Bibliographic record
Abstract
Designing public policies to effectively address comingled economic, social and environmental issues is a fundamental challenge facing sustainable development policy-makers in the twenty-first century. Raising the stakes is the added challenge of doing so in today’s complex, dynamic and uncertain conditions. Policies that cannot perform under such conditions run the risk of not achieving their intended purpose and hindering the ability of individuals, communities and businesses to cope with and adapt to change. To explore the principles of adaptive policies, a four-year empirical investigation was launched in Canada and India to extract practical insights from complex adaptive systems literature and to study the characteristics of policies that have been effective under changing socio-economic and environmental conditions. Seven core principles for creating adaptive policies were identified and a practical policy analysis tool was developed to help policy-makers translate the principles into tangible recommendations. This paper presents the results of applications of the ADAPTool (Adaptive Design and Assessment Policy Tool) by four provincial governments in Canada on policies aimed at supporting climate change adaptation efforts. Lessons learned from the applications are discussed.
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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 | 0.055 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".