Data Practices and Double-Binds of Toronto's Climate Governance
Bibliographic record
Abstract
Contemporary urban climate adaptation and mitigation strategies both draw upon and produce diverse forms of data. As the tenets of data-driven governance are increasingly integrated into city governments' climate policy, careful attention to this intersection is necessary to evaluate how to best leverage data as part of municipal climate action. This project applies a practice-based approach to research, employing a combination of document analysis and semi-structured interviews to identify a set of climate data practices that offer a rich understanding of the role of data in climate politics in the City of Toronto. Together, the practices described in this paper illuminate a set of double-binds created through the pursuit of data-driven climate governance. To help navigate these contradictions we draw on research in HCI, STS, environmental justice, anthropology and urban planning to propose alternative approaches to guide future design and research.
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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 | 0.027 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.018 | 0.036 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".