The Snowden Stakes: Challenges for Understanding Surveillance Today
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The drip-feed disclosures about state surveillance following Edward Snowden’s dramatic departure from his NSA contractor, Booz Allen, carrying over one million revealing files, have ired some and prompted some serious heart-searching in others. One of the challenges is to those who engage in surveillance studies. Three kinds of issues present themselves: One, research disregard: responses to the revelations show a surprising lack of understanding of the large-scale multi-faceted panoply of surveillance that has been constructed over the past 40 years or so that includes but is far from exhausted by state surveillance itself. Two, research deficits: we find that a number of crucial areas require much more research. These include the role of physical conduits including fibre-optic cables within circuits or power, of global networks of security and intelligence professionals, and of the minutiae of everyday social media practices. Three, research direction: the kinds of surveillance that have developed over several decades are heavily dependent on the digital – and, increasingly, on so-called big data -- but also extend beyond it. However, if there is a key issue raised by the Snowden revelations, it is the future of the internet. Information and its central conduits have become an unprecedented arena of political struggle, centred on surveillance and privacy. Those concepts themselves require rethinking.
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.
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it