You Are What Google Says You Are: The Right to be Forgotten and Information Stewardship
Bibliographic record
Abstract
The right to be forgotten is a proposed legal response to the potential harms caused by easy digital access to information from one’s past, including those to moral autonomy. While the future of these proposed laws is unclear, they attempt to respond to the new problem of increased ease of access to old personal information. These laws may flounder in the face of other rights and interests, but the social values related to moral autonomy they seek to preserve should be promoted in the form of widespread ethical information practices: information stewardship. Code, norms, markets, and laws are analyzed as possible mechanisms for fostering information stewardship. All these mechanisms can support a new user role, one of librarian - curator of digital culture, protector of networked knowledge, and information steward.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.051 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".