Bibliographic record
Abstract
The second wave of smart cities emerged in response to criticism of the top-down methods used to manage early smart cities, and promised a new, ‘citizen-centric’ approach. To understand the application of this approach in the smart city planning process there is a need for further empirical research. This paper offers a case study of the participatory planning process used in Quayside, a smart city planning effort in Toronto (Canada). Through semi-structured interviews (N=35), participant observation, and document analysis, this research finds that although Quayside included a lengthy engagement program, citizen influence was limited. This is a result of a lack of participation in initial project visioning, and the direction of the subsequent citizen engagement process by a private technology company, enabled through a public-private partnership. Based on these findings, I argue that a smart city planning process cannot be citizen-centric if citizens are unable to determine project goals. I also suggest that privately-directed engagement processes can amplify the power discrepancies that are well studied within government-directed processes and introduce new accountability challenges.
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.
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 | 0.033 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".