Between the right to know and the right to forget: looking beyond the Google case
Bibliographic record
Abstract
Recently, the EU has demonstrated determination to safeguard the privacy of its citizens concerned with online exposure of their data on the Internet. The Court of Justice of the European Union (CJEU) addressed this concern in a decision against the Internet giant, Google. In this article, this case is placed in the context of a larger debate relating to the ‘right to be forgotten’ and the ‘right to know’. The article argues that the case is not about the victory of privacy rights over the right to know, but rather the upholding of private interest protection when the public interest is absent. Even though in this case the CJEU ruled in favour of the right to be forgotten, it has not dismissed the right to know- it provides safeguards to protect public information from being undermined. The article focuses on the weighing of human rights and the implication for the future of privacy rights and the right to know in the EU. The case is a reminder of the value and the ownership of information in society and educates citizens and companies on how to behave in a digital world. It brings the protection of personal data to a whole new level and may affect the future regulation of Internet companies. Key words: Right to be forgotten, privacy, data protection, information rights, human rights, search engines, the Internet
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.048 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.017 | 0.013 |
| 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".