Assessing Wartime Leaders' Motives: A Comparative Study of the Russo‐Ukrainian War and the World War <scp>II</scp>
Bibliographic record
Abstract
ABSTRACT McClelland's human motivation theory has been used to predict wars and conflicts since its inception. This article offers two novelties. First, the study contextualizes assessments of the imperial motivational pattern by comparing it across countries. Second, it uses an effect size metric, Cohen's d , instead of observed frequencies of power and affiliation words. The resulting assessment can indicate the prospects of negotiation or escalation in a conflict situation depending on the parties' motives. The analysis focuses on the Russo‐Ukrainian War and covers five countries: Russia, Ukraine, the United States, the United Kingdom, and France. The scope of comparisons includes war‐related speeches of those countries' leaders, war coverage by selected mass media outlets, and speeches and news items produced during WWII. Text corpora containing more than 93 million words in four languages (English, Russian, Ukrainian, and French) were processed using a version of the motive lexicon (dictionary). Although the Russo‐Ukrainian War did not reach WWII‐level animosity, the study indicates that the prospects for finding a negotiated solution remain dim. A high “power‐minus‐affiliation” gap characterized the speeches of the belligerent countries' leaders and war coverage by the national media.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".