What’s In a Name? Privacy and Citizenship in the Voluntary Disclosure of Subscriber Information in Online Child Exploitation Investigations
Bibliographic record
Abstract
The Canadian cases dealing with the constitutionality of police access to customer name and address information held by telecommunications service providers (TSPs) are notable in that they deal with voluntary disclosure by TSPs at the request of law enforcement officers and because these requests have all been pursuant to investigations related to child pornography offences. Child exploitation is an exceptional context and we should be cautious in drawing broad legal conclusions from these cases, particularly in relation to the Canadian government’s “lawful access” initiatives which include proposals for mandatory sharing of subscriber information upon police request for any purpose. This article argues that the social and legal context to voluntary cooperation is key to understanding why companies make an exception to their usual practice of requiring warrants in the service of protecting vulnerable children. It also argues that voluntary cooperation must abide by the requirements of “reasonableness” set out in both the Personal Information Protection and Electronic Documents Act (PIPEDA) and Charter jurisprudence. Further, such cooperation must comport with both general privacy principles evaluating the sensitivity of information and the limits on police discretion required to abide by the rule of law.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.048 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| 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".