The Hidden Dimensions of Global Information Networks: What Price Privacy?
Bibliographic record
Abstract
Daily we provide new information about ourselves, when shopping, travelling, communicating on the Internet or telephone, or even when we are simply eating out at a local restaurant. The collection of information is a constant in our lives, with the commercial sector profiling every aspect of our behaviour, from the insurance policies we purchase to the beer we drink. Much of this data is gathered without consumers being aware of the extent to which their privacy and anonymity are being compromised. Whilst the criminal fraternity may also wish to misuse our personal information for fraud or theft, the motives of the largest agencies collating this data are much less obvious or transparent. A recent series of reports to the European Parliament has identified the emergence of new technologies of political control. Such technology can watch and listen to our every move, this is not fiction, although the key player, the US National Security Agency is the same secretive organisation that features in the movie, 'Enemy of the State'. The technology now exists to industrialise such surveillance procedures, with the NSA base at Menwith Hill (UK) having the capability to tap an estimated 2 million phone calls, faxes and emails per hour. Once analysed using artificial intelligence systems such as Memex, this information can be used to build a massive machinery of political supervision with little political oversight or accountability. This paper explores how such information is gathered, the types of documentation now held on us, the way in which it can be manipulated and managed to create universal profiling, and even to change or create virtual images of ourselves. At its heart are the key issues of privacy, dataveillance and political manipulation, which have a wide range of unanticipated consequences and implications that form many of the key concerns of this conference.
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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.018 | 0.069 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".