Emerging Security Technologies and EU Governance
Bibliographic record
Abstract
This book examines the European governance of emerging security technologies.The emergence of technologies such as drones, autonomous robotics, artificial intelligence, cyber and biotechnologies has stimulated worldwide debates on their use, risks and benefits in both the civilian and the security-related fields. This volume examines the concept of ‘governance’ as an analytical framework and tool to investigate how new and emerging security technologies are governed in practice within the European Union (EU), emphasising the relational configurations among different state and non-state actors. With reference to European governance, it addresses the complex interplay of power relations, interests and framings surrounding the development of policies and strategies for the use of new security technologies. The work examines varied conceptual tools to shed light on the way diverse technologies are embedded in EU policy frameworks. Each contribution identifies actors involved in the governance of a specific technology sector, their multilevel institutional and corporate configurations, and the conflicting forces, values, ethical and legal concerns, as well as security imperatives and economic interests.This book will be of much interest to students of science and technology studies, security studies and EU policy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".