Canadian smart cities: health remedies, side effects and hard to swallow pills
Bibliographic record
Abstract
<p>This research paper investigates the ways in which health was talked about and addressed in Infrastructure Canada’s Smart City Challenge. Using the Smart City Challenge applications as the basis of the research, and two as in depth case studies. The main critiques of Smart City Technologies, as well as the concept of Co Creation, and a Performance Measurement Framework were used to identify if the applications could improve, how and if citizens were engaged meaningfully, and where in the healthcare system will the proposed technologies make measurable improvements. Findings from the study indicate there needs to be: greater protections for individual privacy, greater resident engagement/involvement, having health and wellbeing as core nets of a smart city challenge, and greater protections for indigenous data sovereignty. If these recommendations are taken into account, they will lead to more robust applications in the next iteration Smart City Challenge, and will provide invaluable steps towards greater national data guidelines.</p>
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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) | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".