Why government supported smart city initiatives fail: Examining community risk and benefit agreements as a missing link to accountability for equity-seeking groups
Bibliographic record
Abstract
This paper utilizes concepts from a critical social justice discourse on smart cities to identify factors behind resistance to new smart city initiatives from equity-seeking groups. The broader critical discourse is examined based on relevance to the eventual failure of the initiatives selected as case studies. It highlights institutional failure within government-supported initiatives due to the lack of consideration given to equitable distribution of risks and formal accountability mechanisms. It describes outcomes surrounding smart cities in which the benefits accrue to some groups within the city while risks increase for other groups. Finally, we examine the integration of “risk” as an adaptation to the existing practical mechanism of Community Benefit Agreements, for use of this framework to support value sensitive design approaches in future smart city initiatives.
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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 | 0.040 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| 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".