Bibliographic record
Abstract
Intelligence has come to play an increasingly important role in the shaping of policy and policing action around the world. Democratic Oversight of Intelligence Services reflects upon democratic principles applicable to the intelligence sector and the proper oversight mechanisms to install accountability for organisations that operate under a cloak of secrecy. By its very nature, the collection of intelligence also raises a number of ethical and moral questions and appropriate reforms need to be researched, discussed and debated. Reliable and realistic democratic systems of oversight must deal with special executive powers, the requirements of secrecy, the relationship between processes and structures and other hot-potato national security issues. This book addresses the development of, and the challenges and impediments to, democratic oversight and review of the intelligence community in Australia, Japan, Canada, New Zealand, the United States and United Kingdom. The promotion of democratic oversight of the intelligence community has gained renewed significance in the aftermath of 9/11. Legal and administrative frameworks, executive prerogatives and power - and their potential abuses, operational work and analytical tradecraft, crisis management, human rights, state-sponsored detention and interrogation policy and the separation of powers are discussed. ISBN: 9781862877412
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".