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.
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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.065 |
| Scholarly communication | 0.024 | 0.070 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".