E-Toronto: Building a Digital Society
Bibliographic record
Abstract
With the rise of smart city projects around the world, we begin to question “What is a Smart City”? With many smart city initiatives being led by tech giants with corporate agendas, projects often fail to launch. With a focus on big data-driven strategies, these initiatives take on more of a technocratic approach. As a result, these projects often look at citizens as sensors and fail to prioritize the value that people bring to the process of urban development. With data proving to play a key role in the process of decision making, researchers question who has the right to data? And with citizens playing a pivotal role in the process of data collection, how can its value also be shared with those who generate it? \n \nThrough a speculative and critical design approach, this paper explores the question, “What if the citizens of Toronto could begin to control and engage with their data?” Beyond addressing major issues around data privacy, how could the city encourage data-driven participation under open data initiatives? By imagining E-Toronto, a smart city initiative that is citizen-centric, this thesis explores how data can become public infrastructure to support urban development and create new and more contextual experiences for citizens.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".