The use of indicators in environmental policy appraisal: lessons from the design and evolution of water security policy measures
Bibliographic record
Abstract
Drawing up environmental policy options is a complex activity which involves defining and weighing the merits and risks of various alternative courses of action governments could pursue. In its modern version, this task typically involves formal policy analysis or ‘policy appraisal’, that is, policy work specifically undertaken to generate and evaluate policy options in order to address problems or issues on a policy agenda. Indicators play a powerful but under-investigated role in this process. To shed light on this issue, the paper conducts a case study of the design and evolution of policy indicators in water security policy formulation, examining both their utilization and impact. The paper documents the origins of water security policy indicators; assesses their relevance and influence in policy formulation and identifies the reasons for the emergence of certain preferred indices, despite their having several well-known limitations. In particular, the discussion flags the significance of the political advantages surrounding their ease of use and interpretation, rather than their technical merits, as a key factor affecting the continued utilization and influence of specific indicators in environmental policy and planning.
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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 | 0.123 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 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".