Bibliographic record
Abstract
This book provides answers on how allies have to prepare for the strategic challenges in the maritime domain of the 21st century. 24 noted international authors, scholars and practitioners alike, refer to areas of operation and relevant trends and developments. They include the strategic consideration of NATO’s “Northern Flank” as well as an outlook on “Naval Warfare 4.0”. The concise chapters are characterized by their scientific fundament, on which basis recommended actions are drawn. With its substantial practical relevance, this volume is of much value for academics and practitioners in the fields of international relations, security policy, and strategic studies in Germany, Europe, and NATO. With contributions by James H. Bergeron, Keith E. Blount, Sebastian Bruns, Jim Fanell, James Goldrick, Niklas Granholm, Tom Guy, Frank Hoffmann, Sidharth Kaushal, Frédéric Lasserre, Kaspar Pajos, Sarandis Papadopoulos, Chris Parry, Julian Pawlak, Johannes Peters, Pauline Pic, Deborah Sanders, John Sherwood, Dirk Siebels, Jeremy Stöhs, Bruce Stubbs, Sarah Tarry and Alix Valenti.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".