The Credibility of Collective Defense Commitments in NATO in the Early Stages of Trump’s Second Term
Bibliographic record
Abstract
This study is an ad hoc follow-up to the longitudinal investigation of public views on collective defense commitments during the second Trump presidential administration (see “Public Views on Collective Defense in NATO during the Second Trump Administration: A Longitudinal Study” and “NATO’s Collective Defense Credibility during the Second Trump Administration: A Longitudinal Study in Russia” on OSF). We collect survey data three times annually from the following countries: the United States, the United Kingdom, Canada, Poland, Germany, and the Russian Federation. Our primary aim is to test whether Trump’s rhetoric and approach to alliance management lead to discernible shifts in public perceptions of and support for collective defense commitments in NATO. On the one hand, some experts argue that Trump’s approach undermines the credibility of U.S. commitments to NATO allies, which could, in turn, erode the credibility of NATO’s system of collective defense as a whole. On the other hand, some argue that Trump’s unconventional approach will force European allies to make major investments in their defense, which could strengthen NATO’s military capabilities and, consequently, enhance the credibility of collective defense commitments. We propose competing hypotheses to account for these countervailing developments (while noting that they could potentially offset one another, resulting in no net change in public views of collective defense). In the original preregistration, we noted our intention to retain the flexibility to conduct ad hoc surveys following significant future events that may plausibly influence public attitudes in this area. As of early March 2025, we have experienced an unprecedently belligerent rhetoric with respect to the European allies and Ukraine, a cessation of the U.S. military assistance to Kyiv, and a reproachment with the Russian Federation. Many foreign policy commentators have noted that these significant events have already damaged the credibility of U.S. commitments to NATO. This ad hoc round of survey aims to investigate the impact of these events by comparing the current views on collective defense credibility with those we found in our first survey round in January 2025. Furthermore, we will collect new data from samples of the U.S. and U.K. government employees to investigate potential public-elite gaps in these attitudes.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".