“MORE BUZZWORDS THAN ANSWERS” — TO SIDEWALK LABS IN TORONTO
Bibliographic record
Abstract
Many articles have appeared in mainstream media and in techoriented venues about Sidewalk Labs’ ideas for a new hightech neighbourhood in Toronto (a project named Sidewalk Toronto). By and large, international commentary has focused on the opportunities and risks of giving over control over many city planning decisions to a private data-oriented corporation, with people lining up for or against “smart city” ideas, in general. This article will set aside generalities about “smart cities” and technology, and instead pose a few questions about the particulars of Sidewalk Toronto project. The first question concerns the striking lack of transparency of the agreement between Sidewalk Labs (a Google sister company) and Waterfront Toronto, the public authority promoting the project, which is not directly accountable to the city or the citizens. The second question concerns the equally striking ambiguity about which parcel of land is being sought by Sidewalk Labs — an ambiguity that suggests a worrying lack of concern, on the tech company’s part, about both local planning law and local real estate realities. The third set of concerns is about the ownership of the data that appears to be Sidewalk Labs’ real interest. Fourthly, problems in the contract award and procurement mechanisms will be raised. Finally, even though the agreement has not yet been seen even by city council, the process so far and the statements by both parties raise serious concerns about accountability, the fifth point raised in this article.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.656 | 0.390 |
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".