Intelligence Oversight in Times of Transnational Impunity
Bibliographic record
Abstract
This book adopts a critical lens to look at the workings of Western intelligence and intelligence oversight over time and space. Largely confined to the sub-field of intelligence studies, scholarly engagements with intelligence oversight have typically downplayed the violence carried out by secretive agencies. These studies have often served to justify weak oversight structures and promoted only marginal adaptations of policy frameworks in the wake of intelligence scandals. The essays gathered in this volume challenge the prevailing doxa in the academic field, adopting a critical lens to look at the workings of intelligence oversight in Europe and North America. Through chapters spanning across multiple disciplines – political sociology, history, and law – the book aims to recast intelligence oversight as acting in symbiosis with the legitimisation of the state’s secret violence and the enactment of impunity, showing how intelligence actors practically navigate the legal and political constraints created by oversight frameworks and practices, for instance by developing transnational networks of interdependence. The book also explores inventive legal steps and human rights mechanisms aimed at bridging some of the most serious gaps in existing frameworks, drawing inspiration from recent policy developments in the international struggle against torture. This book will be of much interest to students of intelligence studies, sociology, security studies, and international relations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".