Can you standardise transformation? Reflections on the transformative potential of benchmarking as a mode of governance
Bibliographic record
Abstract
This paper is a collaborative effort between academic researchers and practitioners to consider the conditions under which global benchmarking may be used as a tool for supporting urban transformation. Reflecting on WWF’s One Planet City Challenge and UN-Habitat’s Guiding Principles for City Climate Action Planning, the paper suggests that the practice of global benchmarking can be transformative through encouraging organisational learning and reflection, building relationships between cities and global and trans-local organisations, and governing for structurally transformative qualities. However, the practice of benchmarking is not without potential tensions: they may reify existing practices rather than reforming them, be less usable for or accessible to cities in lower income countries, and may neglect issues of climate justice, which are not easily reduced to comparative measures of success or failure. This suggests that a wholesale reliance on benchmarking as a mode of governing climate change might risk marginalising certain issues and amplifying others. We conclude by recommending improved material and technical support for urban data collection and suggest that benchmarking should be combined with a broader suite of performance indicators and reflective practices in order to support urban transformation.
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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 | 0.085 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".