Bibliographic record
Abstract
If surveillance was once thought of as primarily the domain of the nation-state, or of organizations such as firms within the nation-state, in the 21st century it must be considered in a broader context. Surveillance has to do with the rationalized control of information within modern organizations, and involves in particular processing personal data for the purposes of influence, management, or control. It also depends for its success on the involvement of its ‘data-subjects’. In countries of the global north, surveillance expanded with increasing rapidity after computerization from the 1970s onwards, a process that also enabled it to spread more readily to other areas, especially from workers and citizens to consumers and travellers. Since the 1980s, surveillance has become increasingly globalized, as populations become more mobile, and as social relations and transactions have stretched more elastically over time and space. Globalizing surveillance was also catalyzed by the events of 11 September 2001. However, surveillance processes occur differently in different cultural contexts, as do responses to them. Understanding comparatively the various modes of surveillance, understood sociologically, helps us grasp one of the key features of today’s world and also to see political and policy responses to it in perspective.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".