Bibliographic record
Abstract
This article examines the problems associated with data security breaches from two different, but not mutually exclusive, perspectives. The first part of the article examines the need for notification in the event of a data security breach and proposes an amendment of the Personal Information Protection and Electronic Document Act (PIPEDA) to create a legal, or statutory, obligation in Canada to compel disclosure or notification of data security breaches. My recommendations are based on the examination of legislation from other legal jurisdictions, highlighting, where necessary, the shortcomings of the legislation, which ought to be taken into consideration in amending PIPEDA or in drafting a model data security breach notification legislation in Canada.\nThe second part of the article examines the resort to the common law tort of negligence by victims of data security breaches in seeking legal remedy from individuals or organizations whose negligent act(s) resulted in a data spill. While acknowledging that data security breach is a new phenomenon, not yet adequately addressed in common law, I shall go further to show the difficulty in attempts to redress much of the legal claims that come with data security breaches in common law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.013 | 0.084 |
| Scholarly communication | 0.020 | 0.033 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 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".