The frontier of digital opportunity: Smart city implementation in small, rural and remote communities in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.000 | 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