Bibliographic record
Abstract
Society is becoming increasingly more securitized with surveillance technologies having entered a phase of ubiquity, with their components built into many of our daily digital devices. The default state of tracking, monitoring, and recording has fundamentally changed our social and communicative environments. Through the lens of surveillance, everything we do and say can be potentially categorized as a “threat.” Our technological devices become the means by which social control becomes informationalized. A common tool of resistance against these pervasive surveillance practices takes the form of arguing for greater privacy protections to be implemented through information privacy and data protection laws. However, beyond the complexity of the privacy discourse itself, there are diverse information environments not easily parsed by law where the tension between transparency and secrecy complicates privacy practices. The main purpose of this article is conceptual. I consider what the practice of anonymity can offer that privacy does not. From a legal perspective, highlighting the nuances between privacy and anonymity helps us to understand the extent to which our speech and behaviors are becoming increasingly more constrained in the digital environment. In cultural and social contexts, privacy and anonymity often connote differing values; privacy is commonly considered a moral virtue, while anonymity is often maligned and associated with criminal or deviant behavior. In contrast to this understanding, I argue that anonymity should be reconsidered in light of the deterioration of privacy considerations as privacy practices are reframed as contractual resources that are co-opted by both the market and the state. Anonymity, more broadly construed as a mode of resistance to surveillance practices, allows for a more flexible, consistent, and collective means of ensuring civil liberties remain intact.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| 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".