In the name of security secrecy, surveillance and journalism
Bibliographic record
Abstract
The terrorist attacks on the World Trade Center in New York on 11 September 2001 saw the start of the so-called war on terror. The aim of 'In the Name of Security - Secrecy, Surveillance and Journalism' is to assess the impact of surveillance and other security measures on in-depth public interest journalism. How has the global fear-driven security paradigm sparked by 11 September affected journalism? At the core of the book sits what the authors have labeled the 'trust us dilemma'. Governments justify passing, at times, oppressive and far-reaching anti-terror laws to keep citizens safe from terror. By doing so governments are asking the public to trust their good intentions and the integrity of the security agencies. But how can the public decide to trust the government and its agencies if it does not have access to information on which to base its decision? 'In the Name of Security - Secrecy, Surveillance and Journalism' takes an internationally comparative approach using case studies from the powerful intelligence-sharing group known as the Five Eyes consisting of the US, Canada, the UK, Australia and New Zealand. Chapters assessing a selection of EU countries and some of the BRICS countries provide additional and important points of comparison to the English-speaking countries that make up the Five Eyes
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".