Bibliographic record
Abstract
Contextual integrity has now become a (the?) dominant academic theory of privacy. It identifies privacy as both complex and social, two alluring attributes that other leading theories reject. Scholars who engage contextual integrity mostly do so only to convey their confidence in it as their working framework. Even passingly critical notes are rare. This article offers a legal realist critique: Were contextual integrity adopted as a legal standard, it would undermine the very values it was intended to protect, systematically favoring data-hungry corporations at the expense of an already shrinking zone of protected individual privacy. Contextual integrity is dangerous precisely because of the complexity and sociality that draw so many scholars to it. In an adversarial courtroom that pits corporate data interests against aggrieved individuals, these theoretical virtues favor the more sophisticated, well-funded, repeat player.
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 | 0.043 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.083 |
| Scholarly communication | 0.017 | 0.033 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".