A model for the socially smart city practical uses of city-level socio-economic indicators
Why this work is in the frame
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Bibliographic record
Abstract
There is a large amount of discussion in the literature about smart cities where the focus of the discourse is on gathering and analyzing real-time data from smart buildings, smartphones or other sensors to support public services such as vehicular traffic flow, utility consumption or to infer human behaviour. There does not appear to be any discussion of `socially' smart cities where the focus is on using citizens as `smart sensors.' Here the citizens' interactions with a city's services are captured in a timely fashion to derive socio-economic indicators about characteristics of the population relevant to sectors such as education, food security, health, housing, community participation, community safety, income levels and government and to use those as a basis for monitoring community well-being or the effectiveness of government, social service and economic policies designed to produce community improvement. This paper provides the motivation for and outline of a model for a city-level socio-economic indicator system to support the socially smart city. The model is designed to support big highly resolute community data securely. However the model is not just about capturing and analyzing the data; the model must include: deciding what data to collect, developing and communicating with community partners who supply the data and creating a governance structure to ensure that relationships with the data suppliers are maintained. The system will accept timely indicator base data from many different city and other sources and operate on that data using various software tools and maps. The data can be combined in various ways to show single indicators and relationships among indicators. In addition, multiple layers of data can be displayed on a map showing various geographic relationships. An initial version of this model and related system to collect city-level social and economic data and display appropriate socio-economic indicators while protecting individual privacy, is being deployed in a mixed urban-rural community in Southwestern Ontario, Canada. The operational site for the model can be found at myPerthHuron.ca.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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