Knowing and governing cities through urban indicators, city benchmarking and real-time dashboards
Bibliographic record
Abstract
Since the mid-1990s a plethora of indicator projects have been developed and adopted by cities seeking to measure and monitor various aspects of urban systems. These have been accompanied by city benchmarking endeavours that seek to compare intra- and inter-urban performance. More recently, the data underpinning such projects have started to become more open to citizens, more real-time in nature generated through sensors and locative/social media, and displayed via interactive visualisations and dashboards that can be accessed via the internet. In this paper, we examine such initiatives arguing that they advance a narrowly conceived but powerful realist epistemology – the city as visualised facts – that is reshaping how managers and citizens come to know and govern cities. We set out how and to what ends indicator, benchmarking and dashboard initiatives are being employed by cities. We argue that whilst these initiatives often seek to make urban processes and performance more transparent and to improve decision making, they are also underpinned by a naive instrumental rationality, are open to manipulation by vested interests, and suffer from often unacknowledged methodological and technical issues. Drawing on our own experience of working on indicator and dashboard projects, we argue for a conceptual re-imaging of such projects as data assemblages – complex, politically-infused, socio-technical systems that, rather than reflecting cities, actively frame and produce them.
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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 | 0.028 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".