Governance of urban data commons as a matter of value redistribution in the smart city
Bibliographic record
Abstract
In today’s urban environments, “datafication” of social interactions and community activities is ubiquitous and actualises in various applications. One may think of sensor-enabled urban mobility, data-driven water supply systems, innovative waste management plants, and so forth. Data-driven solutions, forming the “smart city”, aim to tackle complex urban problems, and largely depend on marketising or privatising public services. Smart city models, therefore, tend to disguise processes of data appropriation by private enterprises (“data ownership”). By contrast, there is a bourgeoning legal literature exploring how decentralised data infrastructures can open up access to “urban data commons” (UDC). A growing number of public-led (eg the DECODE Project in Barcelona), private-led (eg Sidewalk Toronto in Toronto), and informal projects have put data access into practice. These regulatory schemes aim to foster data access and data sharing, but they tend to neglect the redistribution of value flowing from the positive impact of citizens’ interactions and cooperation on smart city vendors’ activities – what I call “positive externalities”. This paper addresses the issue of data-driven value generation and redistribution in the smart city. It argues that data governance encompasses matters of both use and value that need to be addressed jointly. Therefore, it comes up with some recommendations that can help to incorporate matters of value from data-driven activities. Specifically, I seek to explore the ways to remunerate municipalities in cases where smart city vendors harness positive externalities. In doing so, I circumscribe my analysis to two solutions that have distributional implications for the governance of UDC, ie Fritz Schumacher’s proposal of (large-scale) ownership in his classic Small is beautiful: Economics as if people mattered and the (IP) benefit-sharing principle as applied to indigenous communities.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".