Search Engines and the Right to be Forgotten: Squaring the Remedy with Canadian Values on Personal Information Flow
Bibliographic record
Abstract
The Office of the Privacy Commissioner of Canada (“OPC”) recently proposed that Canada’s private sector privacy legislation should apply in modified form to search engines. The European Union (“EU”) has required search engines to comply with its private sector data protection regime since the much-debated case regarding Google Spain in 2014. The EU and Canadian data protection authorities characterize search engines as commercial business ventures that collect, process, and package information, regardless of the public nature of their sources. Yet both also acknowledge that search engines serve important public interests by facilitating users’ search for relevant information. This article considers specifically what a Canadian right to be forgotten might look like when it is seen as an opportunity to re-balance the values at stake in information flow. This article aims to bring Canada’s existing legacy of balancing important values and interests regarding privacy and access to information to bear on our current information environment.
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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.046 | 0.049 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 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".