On the Benefits of Using Process Indicators in Local Sustainability Monitoring: Lessons from a Dutch municipal ranking (1999–2014)
Bibliographic record
Abstract
Abstract The sustainability performance of cities is subject to an ever‐growing number of monitoring tools. While most initiatives work with outcome indicators that are generally associated with limited direct policy relevance, a minority of tools focuses on sustainability‐related processes and particularly local government policies. In this article, we explore the benefits, limitations and conditions under which this approach can function. While several process‐oriented tools offered to European local governments have lacked participation and foundered, the Local Sustainability Meter (LSM) has been widely used in the Netherlands, with close to 90% of all Dutch municipalities participating since 1999 in some of its multi‐year editions. An evaluative case study presented in this article shows that the LSM stimulated competition for policy performance, conceptual learning and the strengthening of local governance and inter‐municipal networks. The LSM's design choices of combining voluntary, transparent self‐assessments at periodic intervals with public rankings and awards proved to be an effective – and economic – way of disseminating sustainability policies. Its limitations include an inherent focus on generic, standardized policy prescriptions and little knowledge on actual sustainability outcomes. These findings are relevant for policy‐makers and developers of (local) sustainability monitoring tools. This study contributes to the growing literature on (i) sustainability policies and (ii) municipal monitoring and ranking tools. Copyright © 2016 John Wiley & Sons, Ltd and ERP Environment
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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".