Digital Surveillance in the Post-Snowden Era
Bibliographic record
Abstract
Since 2013, we have learned a great deal about the inner workings of the surveillance state of the U.S. and its allies in the Five Eyes (Canada, New Zealand, the UK, and Australia). Through Edward Snowden’s leaks to the press, hundreds of classified National Security Agency (NSA) documents have been made available to the public online. Perhaps most importantly, the Snowden leaks have uncovered relationships between the corporate empire of digital communications platforms and Western intelligence agencies. For example, one internal NSA document demonstrates that Silicon Valley giants such as Google, Facebook, Apple, Yahoo, Microsoft and Skype have shared access to their servers with the NSA through the PRISM program for almost a decade. PRISM and related programs have allowed the Five Eyes to collect and store unprecedented troves of information on their own citizens, including massive amounts of e-mails, text messages, online chats, status updates, phone calls, videos, cellphone location data and search engine history despite constitutional protections against unwarranted searches. As state-run initiatives collect personal data on hundreds of millions of people on an untargeted basis, this thesis questions the scope of their reach in the U.S. and Canada. Has increased public awareness resulted in significant policy reform or have intelligence agencies and corporations continued running the same patterns? This work questions the future of the internet and digital privacy as various entities collect user data for the ultimate purpose of predicting and manipulating user behaviour, both online and in “real life”. As we enter unchartered realms of technological capability, the use of strong encryption and alternative software programs are offered as temporary solutions for securing communications online.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".