Learning from community indicators movements: Towards a citizen-powered urban data revolution
Bibliographic record
Abstract
This paper explores current debates, data products and key implications of what has been called the urban data revolution, which has emerged to international prominence in recent years. We engage with critical appraisals of the new urban data revolution, and discuss what they can learn from both the successes and the failures of the earlier wave of data enthusiasm, the community indicators movement. Second, we analyse the different challenges, dangers and implications of the urban data revolution that both complicate and can sustain a citizen-centred vision of good city governance. We further consider the potential for deliberation and participation in the use of data to define and measure urban progress and success. In the face of a mounting volume and velocity of urban data, these lessons nonetheless pose democratic challenges to the urban data revolution today.
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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 | 0.073 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.021 | 0.034 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.012 |
| 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".