Is security still the chiefest enemy? The challenges and contradictions in European confidence- and security-building in the Cold War
Bibliographic record
Abstract
The regime of Confidence- (and Security-) Building Measures (C(S)BMs) represented an effort to re-imagine Arms Control in Europe and reduce the possibility of unwanted escalation due to misunderstanding or misperception. The regime was first developed during the Cold War due to concerns about large-scale military exercises, and its ongoing importance has come into sharp relief given that NATO and Russia have increasingly engaged in similar manoeuvres. However, despite the C(S)BMs, military exercises represented a point of conflict between NATO and the Soviet Union, and there is little indication that the regime led to the development of confidence in the benign intent of other participants. What prevented this from occurring? This paper compares the theory and logic of confidence-building with the negotiations around the CSBMs, highlighting three primary points of discontinuity that undermined the ability of the regime to fully deliver on its potential. The competitive nature of negotiation about its terms resulted in incomplete transparency, the conflation of the concepts of ‘confidence’ and ‘security’ shifted the focus towards assessing an adversary’s military capability rather than intent, and the regime’s inflexibility meant that it did not account for technological changes that otherwise altered understanding of proximate threat.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".