©2004 Canadian Journal of Communication Corporation Review Essay: Surveying Surveillance Studies
Bibliographic record
Abstract
For scholars of surveillance, as the old curse has it, these are interesting times. With the September 11 attacks on the World Trade Center in New York and the subsequent “War on Terror, ” issues of governmental monitoring, surveillance, and public security have attained sudden prominence in media coverage and public debate. Propelled by post-9/11 fear and loathing, sweeping changes in the legal framework governing surveillance activities, information gathering, and civil lib-erties have been proposed and almost as quickly passed into law in both the U.S. and Canada. Immigration and residency laws have been modified to include new restrictions and reporting requirements for individuals of suspect nationality. Selective law enforcement and patterns of “random ” search procedures have given rise to new allegations of racial and ethnic profiling. Vast and controversial data systems—including the recently unveiled United States Visitor and Immigrant Status Indicator Technology (US-VISIT)—have been introduced to track the movement of foreign elements within the U.S. body politic. “Total information awareness ” has been ripped from the pages of Orwellian fiction and introduced as
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.016 | 0.025 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".