The frontier of digital opportunity: Smart city implementation in small, rural and remote communities in Canada
Bibliographic record
Abstract
Studies of ‘smart cities’ in Canada primarily focus on large cities but not small, rural and remote communities. As a result, we have a limited understanding of the incentive structures for smaller, remote and rural communities to pursue smart city development. This knowledge deficit is concerning, since the introduction of technology can hold a number of unique benefits for these communities, including easier connections to the rest of Canada and large urban centres, reputation building, improved service delivery and enhanced opportunities for residents. Drawing upon localised forms of knowledge creation, policy development theories, adoption and local competition literature and primary interviews with private and public officials, we examine the challenges and opportunities of ‘smart city’ implementation through case studies of small and rural municipalities in Annapolis Valley in Nova Scotia and a remote community, Iqaluit, Nunavut. We find that collaboration is essential for rural and remote pursuit of smart city development and is necessary to counteract the limitations of capacity, scale and digital divides. Challenges aside, however, the primary rationale for adoption of smart city technology remains the same regardless of size: enhanced quality of life for residents and sustained community health.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".