Communities of practice, impression management, and great power status: Military observers in the Russo-Japanese War
Bibliographic record
Abstract
Abstract Military attachés and wartime observers have received surprisingly little attention in international relations. Why do states exchange attachés, permitting uniformed foreigners to gather intelligence on their territory and during their wars? To explain, we adopt a broadly practice-theoretic approach, focusing on the individuals who developed the role by living it, showing how they both innovated a distinct military practice and established institutional legitimacy for attachés. We address an early historical case in which the practice proliferated: the Russo-Japanese War, throughout which observers represented multiple European states, on both sides of the conflict. Sometimes termed the first modern war, the conflict saw Japan's entry into the Eurocentric great power system. In this context, embedded attachés had a dual effect. On the one hand, a professional attaché community established itself: we show how local innovation by embedded officers, in the context of this structurally destabilising event, permitted the creation of a new institutional role that might otherwise have been impossible. On the other, the Japanese made use of the attachés as witnesses for Western governments, observing their performance of great power-hood, as they defeated Russia. The argument has implications for understanding both the military attaché system and communities of practice as such.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".