Beyond the 2% fetishism: studying the practice of collective action in transatlantic affairs
Bibliographic record
Abstract
Abstract NATO burden sharing is currently hotly contested. While it has been measured at the political, economic, and military levels and being looked at from the input and output side, the most commonly used variable to measure NATO BS is considering the percentage of GDP that a country spends on defense, which NATO agreed upon in 2014 should be 2%. The aim of this article is twofold. First, we review the most commonly used system- and state-level variables to explain burden sharing behavior and to carve out their explanatory limitations due to their strong rationality assumptions, positivist epistemologies, deductive, hypothesis testing research designs, and methodological individualism. The gap in the burden sharing literature currently is that it is unable to explain why a particular burden sharing behavior exists (i.e., free-riding) and why it occurred (or not) at a particular point in time. Our second aim is to make suggestions on how to fill these gaps by offering a selective number of post-positivist theories to study NATO burden sharing. We argue that we need to unravel the BS logics and social mechanisms that underpin BS decisions and behaviors, and hypothesize that states may, for example, not exclusively be informed in their burden sharing behavior by a logic of consequentiality but one of appropriateness. However, in order to gain access to this logic and social mechanisms, we need to employ post-positivist theories (and thus methodologies).
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".