Controlled spaces: exploring the integration of smart surveillance technologies in Toronto's privately owned publicly accessible spaces
Bibliographic record
Abstract
<p>Smart technologies are enabling the collection of fine grain data that offer valuable insights to the enhancement of cities. However, these technologies also enable constant and invasive surveillance of individuals in public spaces, and data collected from smart surveillance can be used to control who and how a space is used. This becomes more complex in privately owned publicly accessible spaces (POPS), as these spaces are managed to serve interests defined by private entities rather than by the public. This paper examines how the cities of Amsterdam, Barcelona, New York, Portland and Toronto are managing the use of smart technologies in POPS. The analysis reveals privacy is an increasing concern, and while cities are regulating their own use of smart technologies few are addressing private sector use. Recommendations to the City of Toronto and urban planners are highlighted, along with suggestions for future research.</p> <p><br></p> <p>Key words: Public Space, privately owned publicly accessible space, surveillance, smart</p> <p>technology, smart city</p>
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".