Bibliographic record
Abstract
Since the introduction of the right to be forgotten to European law in 2014, many Western countries have contemplated whether the right could be applied to their citizens. In October 2018, Canada’s Privacy Commissioner asked the Federal Court of Canada to decide if the right is a Canadian fundamental right. However, the right to be forgotten has caused a lot of issues in Europe due to its vagueness and if Canada’s Federal Court rules in favour of making the right a Canadian right, changes will need to be made to it to protect Canadian archives. This paper explores the right to be forgotten and discuss the potential effects the right may have on Canadian archives by exploring the origins of the right, how third-party search engines are currently handling the right, Canadian laws and policies surrounding privacy and the right to know and Canadian archival practices.
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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.054 | 0.029 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.023 | 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".