Characterising and reassessing people-centred data governance in cities
Bibliographic record
Abstract
The increasing deployment of digital infrastructures in cities highlights challenges in how people shape the conditions of data production that shape their cities and lives. As such, the need to centre data governance (DG) models around people is amplified. This paper unpacks and reassesses how people-centredness materialises at the level of DG in cities by conducting a scoping review of the literature on people-centred data governance (PCDG) in cities. Utilising twelve extraction categories framed by the conceptualisation of DG as a socio-technical system, this review synthesises identified themes and outlines six archetypes. PCDG is characterised by people-centred values; the inclusion of people as agents, beneficiaries, or enablers; the employment of mechanisms for engaging people; or the pursuit of people-centred goals. These coalesce into diverse PCDG archetypes including compensation, rights-based, civic deliberation, civic representation, data donations, and community-driven models. The paper proposes a nuanced reassessment of what constitutes PCDG, focusing on whether DG models include people in the emergent benefits of data or merely legitimise their exclusion, the extent to which embedded power dynamics reflect people’s perspectives, the extent to which participation influences decision-making, and the model’s capacity to balance power asymmetries underpinning the landscape in which it is situated.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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 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".