New opportunities in common security and defence policy: Joining PESCO
Bibliographic record
Abstract
Responding to concerns about burden-sharing and aiming to improve internal defence cooperation, act more quickly and harness resource synergies, the European Union (EU) initiated the Permanent Structured Cooperation (PESCO) in 2017. PESCO, however, is controversial. On the one hand, the United States (US) wants greater burden-sharing by European allies whilst concerned about greater European military autarky that would undermine US influence over NATO, Europe/EU and EU member states. Onthe other hand, at least one European NATO ally wants to leverage PESCO precisely as an instrument to shore up European “strategic autonomy”. This tension over competing European defence futures leaves participation by third countries in limbo. Arguably, third-country participation would hinder greater European defence autarky. The article makes the case for the mutual benefits of third-country participation, focusing on Canada. Canada has a major stake in the outcome. NATO is Canada’s most important multilateral institution and Europe is Canada’s second-most important strategic partner, after the US. Canada’s unequivocal strategic interests in Europe have long informed its expeditionary priorities -- from the two world wars, when Canada coming to Europe’s defence long before the USproved existential for both parties, to nowadays. Since the 1970s, Canada and Europe have worked consistently together bilaterally beyond NATO to advance regional stability and mutual security interests. Canada’s and Europe’s defence futures are thus interdependent. Excluding third countries from participating in PESCO would have detrimental consequences for Canadian, European and transatlantic defence interests. In contrast, with third country participation, PESCO will be instrumental to effective transatlantic and transeuropean defence integration.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.020 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".