Reconceptualising burdens – NATO centres of excellence: club goods, informal institutions, and partner contributions
Bibliographic record
Abstract
Research on NATO burden sharing comprises dozens of works. Since the end of the cold war, NATO adapted, enlarged, and institutionally complexified its mandates and area of operations. Yet, burden sharing research rarely accounts for such aspects given the political and scholarly focus on NATO’s target of 2% of GDP on military spending since the 2000s. This research merges rational institutionalism with collective action models on club goods production to study NATO Centres of Excellence. These 30 institutions are externally funded, independently run, and address the collective strategic problems associated with Alliance Transformation using informal arrangements, the Memorandum of Understanding. The contents of those agreements are compared using rational institutional design theory. This article offers original data reconceptualising the NATO burdens partners undertake beyond military expenditures using COE participation and hosting while offering a framework for a future examination of how the COE institutional agreements manage strategic problems (i.e. enforcement, allocation, uncertainty, etc.) with expanded data.
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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".