Bibliographic record
Abstract
Open Data are recognised as invaluable resources at the city level for improving local services, community engagement and businesses initiatives, but their use still struggle to have the desired impact. This work addresses the underuse of Open Data by exploring the connection between data and actions in everyday urban activities implemented by local governments, public agencies, businesses, non-profit organisations and research institutions operating in the city. The empirical results of this exploratory study outline a structural misalignment between a) roles of local actors in city activities and their data-related activities, b) provision of Open Data and information needs of local actors, c) expected uses of data in local actions and forms of support to the users provided by current city Open Data portals. The envisioned alternative approach to foster the use of Open Data at the city level rely on identifying the appropriate data to be produced for supporting local actions, instead than focusing on publishing data disconnected from real information needs of organisations working for local communities.
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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 | 0.032 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.020 | 0.031 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.035 |
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".