Abolish privacy
Bibliographic record
Abstract
This is a manifesto to reject calls to “online privacy.” Privacy is an inherently exclusionary liberal entitlement inextricably linked to property, racial oppression, sexual control and class segregation. However online privacy is uncritically promoted by academics, activists and media discourse as a civil right and even a form of social justice. We build on the arguments of feminist, queer, Black Radical traditions and GDPR legal scholars to make three arguments to the contrary. First, privacy is not social justice, but reproduces the ideologies of colonial rankings, policing and norms (racial, sexual, able-bodied and more). Second, online privacy is inextricable from private property as the exclusive legal protection of home-owners from state oversight; a liberal privilege that is easily appropriated into corporate affordances. Third, online privacy is a myth: the Internet works because its infrastructures share knowledge inferred from data, in other words there is no actual possibility of privacy, nor any real corporate incentive to privacy, except as an optic tactic to avoid accountability. Grounded in anti-capitalist social justice, our argument exceeds current liberal civil definitions of safety, such as the Human Rights based approach to Smart Cities inspired by the United Nations, the GDPR, municipal policies and environmental law. Instead we call for public, collectivized data and the complete rejection of liberal property entitlement, gatekeeping and policing known as “privacy.”
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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".