When Google Becomes the Norm: The Case for Privacy and the Right to be Forgotten
Bibliographic record
Abstract
The ubiquity of the Internet is inescapable; from online banking and document transmission to social media and video communications, the digital world is becoming increasingly populated. Collective connectivity brings with it unique legal and regulatory challenges that did not exist in a pre-Internet era, particularly given the Internet’s inherent technical complexities and issues around territorial jurisdiction and competing rights and values. The divide between the law and societal expectations is particularly noticeable when considering individual privacy; when personal information is easily accessible by millions of users around the globe with access to a modem or a mobile network, is there any recourse available for someone wishing to limit their personal exposure? This paper will consider the so-called "Right to be Forgotten," enshrined in European law since 2014 but still a foreign concept in Canada. In doing so, the paper queries whether the ability for a party to apply to Google to have damaging personal information de-listed from its search algorithm would be legally possible in light of the Charter, and whether it would even be desirable from a policy perspective
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 | 0.023 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.078 |
| Scholarly communication | 0.030 | 0.037 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.039 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 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".