Introduction, Chapter 1.Intelligence and Oversight at the outset of the 21st century and Chapter 5. Reappraising Intelligence Oversight in the UK
Bibliographic record
Abstract
This book examines how key developments in international relations in recent years have affected intelligence agencies and their oversight. Since the turn of the millennium, intelligence agencies have been operating in a tense and rapidly changing security environment. This book addresses the impact of three factors on intelligence oversight: the growth of more complex terror threats, such as those caused by the rise of Islamic State; the colder East-West climate following Russia’s intervention in Ukraine and annexation of Crimea; and new challenges relating to the large-scale intelligence collection and intrusive surveillance practices revealed by Edward Snowden. This volume evaluates the impact these factors have had on security and intelligence services in a range of countries, together with the challenges that they present for intelligence oversight bodies to adapt in response. With chapters surveying developments in Norway, Romania, the UK, Belgium, France, the USA, Canada and Germany, the coverage is varied, wide and up-to-date. This book will be of much interest to students of intelligence studies, security studies and International Relations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.115 | 0.046 |
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".