Indicators and metrics in local climate adaptation plans
Bibliographic record
Abstract
This dataset gathers information related to indicators and metrics collected from 11 local climate adaptation plans in worldwide cities - Athens (Greece), Auckland (USA), Barcelona (Spain), Glasgow (UK), Istanbul (Turkey), Lima (Peru), Los Angeles (USA), Montreal (Canada), Nagoya (Japan), New York City (USA), Portland (USA), Tokyo (Japan) and Vancouver (Canada). The dataset describes the use and characteristics of adaptation indicators and metrics across climate adaptation-related planning documents. Although the sample is relatively small, it is a reflection of the global embryonic stage of adaptation metrics practice. The results of the analysis of this database have been published in: Goonesekera, S. M., & Olazabal, M. (2022). Climate adaptation indicators and metrics: State of local policy practice. Ecological Indicators, 145, 109657. https://doi.org/10.1016/j.ecolind.2022.109657 (OPEN ACCESS)
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.014 |
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".