A participatory approach for empowering community engagement in data governance: The Monash Net Zero Precinct
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Data governance is an emerging field of study concerned with how a range of actors can successfully manage data assets according to rules of engagement, decision rights, and accountabilities. Urban studies scholarship has continued to demonstrate and criticize lack of community engagement in smart city development and urban data governance projects, including in local sustainability initiatives. However, few move beyond critique to unpack in more detail what community engagement should look like. To overcome this gap, we develop and test a participatory methodology to identify approaches to empowering community engagement in data governance in the context of the Monash Net Zero Precinct in Melbourne, Australia. Our approach uses design for social innovation to enable a small group of “precinct citizens” to co-design prototypes and multicriteria mapping as a participatory appraisal method to open up and reveal a diversity of perspectives and uncertainties on data governance approaches. The findings reveal the importance of creating deliberative spaces for pluralising community engagement in data governance that consider the diverse values and interests of precinct citizens. This research points toward new ways to conceptualize and design enabling processes of community engagement in data governance and reflects on implementation strategies attuned to the politics of participation to support the embedding of these innovations within specific socio-institutional contexts.
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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 | 0.003 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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 it