Beyond Privacy: Articulating the Broader Harms of Pervasive Mass Surveillance
Bibliographic record
Abstract
This article begins by recounting a series of mass surveillance practices conducted by members of the “Five Eyes” spying alliance. While boundary- and intersubjectivity-based theories of privacy register some of the harms linked to such practices I demonstrate how neither are holistically capable of registering these harms. Given these theories’ deficiencies I argue that critiques of signals intelligence surveillance practices can be better grounded on why the practices intrude on basic communicative rights, including those related to privacy. The crux of the argument is that pervasive mass surveillance erodes essential boundaries between public and private spheres by compromising populations’ abilities to freely communicate with one another and, in the process, erodes the integrity of democratic processes and institutions. Such erosions are captured as privacy violations but, ultimately, are more destructive to the fabric of society than are registered by theories of privacy alone. After demonstrating the value of adopting a communicative rights approach to critique signals intelligence surveillance I conclude by arguing that this approach also lets us clarify the international normative implications of such surveillance, that it provides a novel way of conceptualizing legal harm linked to the surveillance, and that it showcases the overall value of focusing on the implications of interfering with communications first, and as such interferences constituting privacy violations second. Ultimately, by adopting this Habermasian inspired mode of analysis we can develop more holistic ways of conceptualizing harms associated with signals intelligence practices than are provided by either boundary- or intersubjective-based theories of 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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.076 |
| Scholarly communication | 0.015 | 0.032 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".