Developing Privacy Extensions: Is it Advocacy through the Web Browser?
Bibliographic record
Abstract
In 2015, Edward Snowden recommended that ordinary citizens use adblocking software and an encryption-oriented privacy extension for their web browser to protect against surveillance. This paper critically explores how the development of privacy extension software for web browsers can be situated in relation to privacy advocacy. Privacy advocates are individuals who act on behalf of the citizenry to speak to governments and corporations about how our data are collected and processed. While the Snowden revelations began in 2013 and the Cambridge Analytica scandal broke in 2018, privacy extensions remain underexplored in the literature on privacy advocacy. This paper shares findings from ethnographically informed interviews conducted with thirty developers and other knowledgeable experts who created twenty-six named privacy extensions. The privacy extensions explored included anti-tracking, hypertext transfer protocol secure (HTTPS), and privacy policy and password-related functionalities. Although privacy extensions are an imperfect set of tools to protect privacy, we argue that production of this software demonstrates an array of privacy advocacy strategies. Privacy extensions can be scaffolded upon previous resistance moves to surveillance by individuals, while also sometimes intersecting with traditional and expanded notions of collective action in the digital age.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".