Bibliographic record
Abstract
Most people seem to agree that individuals have too little privacy, and most proposals to address that problem focus on ways to give those users more information about, and more control over, how information about them is used. Yet in nearly all cases, information subjects are not the parties who make decisions about how information is collected, used, and disseminated; instead, outsiders make unilateral decisions to collect, use, and disseminate information about others. These potential privacy invaders, acting without input from information subjects, are the parties to whom proposals to protect privacy must be directed. This Article develops a theory of unilateral invasions of privacy rooted in the incentives of potential outside invaders. It first briefly describes the different kinds of information flows that can result in losses of privacy and the private costs and benefits to the participants in these information flows. It argues that in many cases the relevant costs and benefits are those of an outsider deciding whether certain information flows occur. These outside invaders are more likely to act when their own private costs and benefits make particular information flows worthwhile, regardless of the effects on information subjects or on social welfare. And potential privacy invaders are quite sensitive to changes in these costs and benefits, unlike information subjects, for whom transaction costs can overwhelm incentives to make information more or less private. The Article then turns to privacy regulation, arguing that this unilateral-invasion theory sheds light on how effective privacy regulations should be designed. Effective regulations are those that help match the costs and benefits faced by a potential privacy invader with the costs and benefits to society of a given information flow. Law can help do so by raising or lowering the costs or benefits of a privacy invasion, but only after taking account of other costs and benefits faced by the potential privacy invader.
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 | 0.019 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".