Bibliographic record
Abstract
Abstract The first chapter of the volume draws on the country chapters to offer a number of reflection points on the current nature of the Alliance and, most specifically, its relevance and cohesion. It first builds a typology of allies based on how they see, and behave within, the Alliance. Three categories of ally are identified: the ‘non-NATO-aligned with a broad security agenda’, a group composed of the United States, France, and Turkey; the ‘NATO-aligned with a Russian-centric security agenda’, a group composed of the three Baltic States, Norway, Poland, and Romania; and the ‘NATO-aligned with a non-Russian-centric security agenda’, which include the United Kingdom, Germany, Italy, Canada, Denmark, the Netherlands, and Spain. Second, the chapter draws on the subsequent country analysis to identify seven key findings that characterize contemporary NATO: these range from the persistent need for NATO, the nature of the bilateral link with the United States, and the centrality of collective defence, to the debate about task expansion, the existence of alternatives to NATO, the importance of values, and the level of strategic thinking within allies. Third, the chapter reflects on those findings to assess the level of relevance and cohesion that the Alliance enjoys. In many ways, NATO’s relevance and cohesion are being challenged by a complex security environment, yet it is largely this complexity that makes the Alliance still essential to its Nations.
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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 | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".