Achieving interoperability of smart city data: An analysis of 311 data
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
A major challenge in making cities smarter is performing comparative analyses across two or more cities, or within a city across two or more departments. The problem is that data models and the underlying semantics of their content differ, making analysis difficult at best and erroneous at worst. This paper explores the hypothesis that a single, interoperable (i.e., shareable) data model/ontology can be designed for one category of city data: openly published 311 call centre data. 311 is a service provided by many North American cities that responds to non-emergency questions and reports made by the public. It has rapidly become the single point of contact for city services, inquiries, etc. We perform a semantic analysis of the content of 311 open datasets from four cities. The result of the analysis is that existing 311 datasets combine multiple semantic dimensions in their data making it impossible to perform comparative analysis. We then construct a 311 Reference Ontology that separates the semantic dimensions, and show how 311 data from multiple cities can be mapped onto the 311 Reference Ontology. We also demonstrate how the ontology can be used to support analysis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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