Bibliographic record
Abstract
The book takes a multi-disciplinary approach to explore the role national security intelligence agencies played in supporting national governments’ response to COVID-19. Spanning the ‘Five Eyes’ intelligence countries (UK, USA, Canada, Australia and New Zealand), this book offers the first cross-comparative analysis of what intelligence agencies need to focus on in responding more effectively to future emerging health and biological security threats risks and hazards post-COVID-19. The volume addresses three principal issues. First, it investigates what roles the Five Eyes intelligence communities played (along with other key stakeholders, such as public health agencies) in managing the COVID-19 pandemic. Second, it assesses the challenges of and lessons learnt from these intelligence communities’ engagement in managing aspects of the pandemic. Third, it explores how the Five Eyes might play more effective roles in managing future health security threats and risks, whether those are intentional (bioterrorism and bio crimes), accidental (laboratory releases) or unintentional (pandemics) in origin. Overall, this book offers a coherent and holistic research agenda that seeks to improve understanding about the role of national security intelligence in managing health security threats and risks post-COVID-19. This book will be of much interest to students of intelligence studies, health security, public health and International Relations. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.780 | 0.729 |
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".