Bibliographic record
Abstract
Abstract Democracy needs to be defended, and intelligence is the first line of defence. However, the liberal-democratic norm of limited state intervention in the lives of citizens means that security and accountability are in tension insofar as their first principles are diametrically opposed: whereas openness and transparency are hallmarks of democratic governance, operational secrecy—in relation to other states, to democratic society, and to other parts of government—is the essence of intelligence tradecraft. Intelligence accountability reconciles democracy and security through transparent standards, guidelines, legal frameworks, executive directives, and international law. Evolving executive, legislative, judicial, and bureaucratic mechanisms for intelligence oversight and review have become a distinct feature of democratic regimes. Over recent decades legislative and judicial components have been added to complement administrative and executive accountability. Using a most-similar systems design to compare intelligence accountability in the United States, the United Kingdom, Canada, Australia, and New Zealand, this book expands compliance as the sine qua non of intelligence to gauge effectiveness, efficiency, and innovation across the intelligence community. In the context of changing technology and threat vectors that have significantly affected, altered, and expanded the role, powers, and capabilities of intelligence, this book compares the institutions, composition, practices, characteristics, and cultures of intelligence accountability systems across the world’s oldest and most powerful intelligence alliance. In an asymmetric struggle against unprincipled adversaries, accountability has to reassure a sceptical public that the intelligence and security community plays by the same rules that democracies are committed to defend.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".