Just smart? The comparative analysis of smart initiatives in North America and Nordic countries
Bibliographic record
Abstract
There is a strong belief in the policy and academic worlds that the smart city concept can pave the way for inclusive urban governance by promoting citizen engagement. However, smart initiatives may strengthen the existing or create new inequalities, undermining the ability of underrepresented groups to influence urban governance decisions. This study critically examines this dilemma by analyzing eight smart initiatives across four Arctic cities: Bodø (Norway), Oulu (Finland), Fairbanks (USA), and Yellowknife (Canada). Through a comparative framework that accounts for the unique justice issues in the Arctic, such as severe climate challenges and the legacy of Indigenous suppression, we analyze qualitative open data available in the local media and on official web resources and assess whether and how these initiatives foster a just society. Our findings indicate the existing high potential of human-centric smart initiatives to enable public engagement. However, the study concludes that smart city projects often fail to adequately prioritize underrepresented groups’ interests, like those of Arctic Indigenous communities. This underscores the urgent need for further research into the intersections between smart city development and the principles of a just city.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".