Bibliographic record
Abstract
Toronto's Portlands neighborhood is the target of an enormous redevelopment effort that will infuse smart-city technologies into the urban morphology. The quasi-governmental Waterfront Toronto agency has partnered with the Alphabet subsidiary company Sidewalk Labs to plan and build out the neighborhood, essentially from the ground up, and embedded it with sophisticated technologies. The redevelopment plan details a digital layer made up of sensors that will collect and process locational information, tracking movement and usage patterns. Yet the project has been mired in controversy, mainly because of questions about data ownership and management. While there will be the amassing of an unfathomable amount of data, it is not clear who will control it and how it will be processed and used. Critics of the project have pointed out that the potential value of the data is enormous and if a private company has exclusive domain over it, that company could decide to sell it at will. Securely storing citizen’s data is another problem. This paper provides a description of the popular newspaper accounts of the Waterfront Toronto Project. It discusses how the project and redevelopment authority came to be, how Alphabet be-came the primary partner, the redevelopment vision, and controversy that has engulfed this smart-city project.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".