‘Unsolvable within existing regimes’: Using a Systems Thinking Approach to Co-design for Data Governance in Cities
Bibliographic record
Abstract
Despite people’s significant role in generating data in cities, their involvement in data governance (DG) remains limited, failing to address the inherent complexity of DG and undermining their ’right to the city’. We propose a collaborative systems thinking approach as a scoping tool for co-design, enabling researchers and designers to involve people in co-creating an understanding of the systemic structures underpinning DG in cities and developing prototypes and solutions informed by these structures. Using causal loop diagrams, we facilitated the development of a conceptual model of DG. Participants, representing diverse perspectives, created individual causal loop diagrams that were merged into a collaborative causal loop diagram (C-CLD). This C-CLD was employed in an interactive workshop to identify intervention points and develop targeted solutions. Our findings demonstrate how C-CLDs can accommodate multiplicity, foster agonism, and enable participants to challenge political dimensions and existing systemic structures. Moreover, the engagement process revealed the complexity of DG in the city, as perceived by the collective of participants, resulting in three key submodules that highlight tensions between citizen sensitisation to data collection, the private sector’s role in fulfilling citizens’ needs, and the struggles faced by local governments. This work draws on and extends HCI research that engages with systems thinking ontologies, contributing to an HCI that includes the political, moves beyond solutionism, and advances social justice-oriented approaches.
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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 | 0.001 | 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 | 0.001 | 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".