Bibliographic record
Abstract
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 Tracey P. Lauriault (Carleton University), 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.Authors: Tracey P. Lauriault (Carleton University), Rachel Bloom (OpenNorth) and Jean-Noé Landry (OpenNorth).Funded by Natural Resources Canada’s GeoConnections program in 2018.
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.
How this classification was reachedexpand
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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".