A Compromise of Values, Privacy, and Protection: Exploring Sidewalk Toronto’s Failure to Launch Through an Intersectional Lens of Energy Justice, Privacy, and Data
Bibliographic record
Abstract
Energy justice is a renewable energy transition theory that encourages participation of marginalized communities in energy decision-making processes. Energy justice recognizes that non-renewable energy systems unfaily place the burden of pollution and environmental degradation on the surrounding communities. The allocation of these energy system burdens is not accidental and often targets racial, low-income, Indigenous, and other types of marginalized communties. There is a need for data and personal information in order to identify instances of energy injustice. Generally, intellectual property law governs privacy and data. Sidewalk Toronto promised to be a new, inclusive, affordable, climate positive development. Yet, the many privacy and data concerns that this project raised over its short span led it to be unfeasible. The vague terms, ineffective public consultation, and the ever expanding scope of Sidewalk Toronto were key features that accounted for its failure. Additionally, the privacy legislation in Canada is out of date and no longer adequately protects consumers in Canada. Energy justice depends on strong privacy protection of the same marginalized communities already burdened by energy systems. This paper offers remedies that could be applied to similar future smart-city proposals.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.033 | 0.028 |
| Scholarly communication | 0.023 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".