Can you standardise transformation? Reflections on the transformative potential of benchmarking as a mode of governance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it