Bibliographic record
Abstract
The North Atlantic Treaty Organisation (NATO) is responding to security challenges arising from emerging technologies by leveraging national and multilateral innovation networks to strengthen collective defence and accelerate technological adaptation. NATO partnerships provide a research scope encompassing key policy developments on specific technologies, including artificial intelligence, autonomy, quantum, and data. Poland’s innovation ecosystem is analysed to highlight how public–private partnerships with NATO promote alliance-wide strategic objectives aligned with national innovation networks. This comparative case study analyses NATO’s Defence Innovation Accelerator for the North Atlantic (DIANA) and the national innovation networks of allies, as demonstrated by the case of Poland. Data was gathered from policy documents, industry reports, and other publicly available sources. Poland’s proactive innovation strategy is a model for utilising local strengths to tackle global security concerns and test facilities and accelerator programmes under NATO DIANA. Poland’s innovation model provides a significant case study that offers valuable insights for future research and development. Combating the challenges posed by evolving technologies requires adaptable security measures, demonstrated by various examples from Poland’s local accelerators and test centres. NATO’s approach, involving industry partnerships and defence innovation, provides a model for other allies. The case of innovation in Poland provides a generalisable methodology for studying other national approaches to innovation aligned with the NATO accelerators.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".