Bibliographic record
Abstract
In the last decade, the evolution of the breach of confidence as a legal instrument to redress harms occurring in digital realms has tested the limits of this cause of action and raised significant questions about the legal interests it serves to protect. This case comment focuses on another type of dispute—one where courts have been far more equivocal in their approach—where parties have sought to assert the breach of confidence for wrongs occurring in online relationships: data breaches. Focusing on Tucci v. Peoples Trust Company, a judgment handed down by the Court of Appeal for British Columbia in September 2020 on appeal of a decision certifying a class of web users whose personal information was subject to unauthorized acquisition in a data breach, this comment scrutinizes the reading of the breach of confidence that Canadian courts have been making in the context of data breaches, and contends that this reading ignores the essence and promise of this cause of action to instill trust in online relationships that are threatened when data breaches occur. Contrary to judicial reluctance to allow the breach of confidence to operate in these scenarios, this comment argues that this cause of action is an appropriate and effective mechanism for establishing and reinforcing norms of trust in online relationships that are threatened when data breaches occur.
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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 | 0.021 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.072 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.027 | 0.029 |
| Insufficient payload (model declined to judge) | 0.007 | 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".