Bibliographic record
Abstract
This document intends to guide city stakeholders (public officials, civil society, technology innovators, private sector, academics, residents, etc.) toward formulating and strategically aligning practices with their agreed upon and unique Open Smart City vision. The Open Smart City definition and guide are relevant to city leaders and community leaders at multiple levels of governance (e.g., provincial, territorial, and federal). This is part of a series of documents from the Natural Resources Canada GeoConnections funded Open Smart Cities project at Open North and led by Dr. Tracey P. Lauriault in collaboration with Jean-Noe Landry and Rachel Bloom of Open North. Open Smart Cities in Canada is a collaborative project. We would like to thank smart city representatives from the cities of Edmonton, Guelph, Montréal, and Ottawa and officials from the provinces of British Columbia and Ontario for sharing their time, expertise, and experiences with us. Furthermore, this project benefits from contributions made by the project’s core team of experts and researchers. We are grateful to Professor David Fewer, LL.M., (Canadian Internet Policy and Public Interest Clinic (CIPPIC)), and Professor Mark Fox (University of Toronto) for providing their expert advice on the design of research and its outputs. Finally, we thank graduate students Stephen Letts and Carly Livingstone (Carleton University) for research assistance and editing over the course of the project.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.328 | 0.299 |
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".