Bibliographic record
Abstract
Unlike the commonplace statement that defamation law protects reputation, this article suggests that it only protects aspects of reputation. Previously, defamation was often the only avenue of legal protection for reputation worth examining, but now privacy actions also offer an avenue of protection for aspects of reputation in many jurisdictions. In other words, informational privacy law now protects aspects of reputation, as does defamation law. Recognizing this fact leads to the suggestion that exactly what each action—defamation and informational privacy—seeks to protect could be stated more concisely. This exercise, undertaken in this article, draws on classic defamation law analysis by Thomas Gibbons. Restating the law in this way would reform defamation law, clarifying and simplifying how it and privacy law coexist, and could offer a useful path for addressing more technical arguments about the boundaries between the actions or the ways in which they should be reconciled.
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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".