Canadian Journal of Communication Vol 39 (2014) ©2014 Canadian Journal of Communication Corporation Reviews
Bibliographic record
Abstract
Liquid Surveillance is the product of a series of email conversations between Zygmunt Bauman and David Lyon that took shape between September and November 2011. The book is structured around ques-tions posed by Lyon; however, Lyon’s role is more than that of inter-viewer. As Bauman and Lyon are both highly influential scholars and social theorists, there is a great deal to recommend about this book. As the title of the text attests, the book picks up on Bauman’s earlier work on liquid modernity as a framework for thinking about contemporary surveillance. In the introduction to the text, Lyon explains the importance of the liquid metaphor in con-trasting the “mobile, pulsating signals of today’s flowing forms ” with the “fixity and spatial orientation of solid modern surveillance ” (p. 15). Using liquidity as a premise, the book’s seven chapters are organized into themes around which the conversations took shape. The themes addressed include drones and social media; liquid surveillance as post-panoptic; remoteness, distancing, and automation; in/security and surveil-lance; consumerism, new media, and social sorting; probing surveillance ethically;
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.146 | 0.028 |
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".