The Automation of the Softer Side of Smart City: a Socio-Semantic Roadmap
Why this work is in the frame
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Bibliographic record
Abstract
The Automation of the Softer Side of Smart City: a Socio-Semantic Roadmap Tamer El-Diraby, Alain Zarli and Mohamed El-Darieby Pages 258-265 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: We present a roadmap for guiding public officials on establishing platforms for citizen empowerment in the smart city. The proposed roadmap is not a technical architecture. Rather, a set of paradigms, guidelines and references to advanced technology approaches that can support building a technical architecture. We start from the perspective that the smart city architecture is not a venue for services, but a domain of innovation. We advocate encouraging citizen science to co-create new solutionsin contrast to engaging them to inform them or to evaluate solutions developed by professionals. We advocate giving equal attention to structured and unstructured data analysis. We also encourage the adoption of adaptable data orchestration tools to help navigate and organize the complexity of city data. Finally, we provide an outlook on the future trends (such as Blockchain and cognitive computing) in urban systems decision making. Keywords: Smart city; Citizen science; Socio-semantic analysis DOI: https://doi.org/10.22260/ISARC2019/0035 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.001 | 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