The Surveillance Legacies of 9/11: Recalling, Reflecting on, and Rethinking Surveillance in the Security Era
Bibliographic record
Abstract
Do we need yet more analysis of the responses to the September 11, 2001 (hereafter 9/11), terrorist attacks? Those tragic events occurred more than a decade ago, and their 10-year memorial focused on bringing “closure” to the event. For many, those attacks have become an increasingly distant, if still poignant, memory. For still others—such as the new cohort of undergraduate students who were only nine years old on the day of the attacks—9/11 is social history. Our contention in putting together this volume is that there continues to be significant reason to scrutinize 9/11 in terms of its consequences for the dynamics of surveillance. The aftermath of that tragic event played a major role in policy changes and in international relations. Wars were fought in Iraq and Afghanistan, sparked by 9/11, and many thousands more people died as a result. “National security” was elevated to a top priority in the United States and elsewhere, and this approach has had wave and ripple effects throughout the world. This is the “War on Terror,” and, unlike other wars, this one has no visible end point. These developments certainly affected surveillance practices internationally and have been the cue for the United States to demand that other countries fall in line with its approach. On the other hand, for many countries, especially in the global south, 9/11 is not a top-of-mind matter, nor is “national security” a vital concern.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.045 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".