Open Data Insights from a Smart Bridge Datathon: A Multi-Stakeholder Observation of Smart City Open Data in Practice
Bibliographic record
Abstract
“Open Data” efforts are growing, especially in Europe, where open data are seen as a possible ethical driver of innovation. As smart cities continue to develop, it is important to explore how open data will affect the stakeholders of smart public spaces. Making data open and accessible not only has a managerial and technical component but also creates opportunities to shift power dynamics by granting individuals (and entities) access to data they might not otherwise be able to obtain. The scope of those who could access these data is wide, including data-illiterate citizens, burgeoning startups, and foreign militaries. This paper details the process of making data “open” from the MX3D smart bridge in Amsterdam through a “datathon”. The development and outcomes of opening the data and the event itself bring us closer to understanding the complexity of open data access and the extent to which it is useful or empowering for members of the public. While open data research continues to expand, there is still a dearth of studies that qualitatively detail the process and stakeholder concerns for a modern smart city project. This article serves to fill this gap.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".