Citizen Participation through Digital Platforms: the Challenging Question of Data Processing for Cities: Proceedings of the Eighth International Conference on Smart Cities, Systems, Devices and Technologies
Bibliographic record
Abstract
This paper focuses on digital platforms supporting citizen participation in the era of Smart Cities. Our study presents and analyses two examples of online participation platforms, implemented by two Walloon cities: Mons and Liège (Belgium). These two cases highlight the differences and the similarities between both cities' interpretation of digital participation, as well as the difficulties they faced, especially considering the data processing by city officials. In light of the challenges observed through those two cases, we suggest that digital platforms might potentially be misused, and somehow bias the whole digital participatory process. We therefore issue recommendations about how to design, launch and manage such platforms and, moreover, suggest that platforms should be supplemented by other digital or traditional participatory processes in order to reach higher levels of participation.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".