Bibliographic record
Abstract
Drawing on expertise from professionals, government officials, and academics, this book uncovers the proactive measures taken by Latvia to build resilient cybersecurity capabilities. The work offers a comprehensive exploration of Latvia’s cyber domain, structured around three overarching themes: the ecosystem, its processes, and future perspectives. In doing so, it takes readers through the intricacies of Latvia’s cybersecurity landscape and provides a nuanced understanding of its strengths, challenges, strategic considerations, and broader implications. One of the key contributions of the work lies in its exploration of Latvia’s cybersecurity strategies and resilience. By delving into the nation’s policies, collaborations, and technological advancements, this book uncovers how Latvia has proactively addressed cyber threats, emphasising the importance of tailored approaches for smaller countries in building robust cybersecurity defences. Highlighting the importance of studying cybersecurity in smaller nations, this book stresses Latvia’s contributions to global cybersecurity efforts as an EU and NATO member. The volume advocates for innovation and collaboration, emphasising their crucial role in securing a digital future for nations worldwide. This book will be of much interest to student of cybersecurity, Baltic politics, EU politics, global governance, 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-Share Alike (CC-BY-NC-SA) 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".