The effects of wars: lessons from the war in Ukraine
Bibliographic record
Abstract
War remains a central feature of global politics and has been a core focus for politics and international relations, history, economics, sociology as well as other cognate disciplines. The analysis of the effects of war has, however, tended to be compartmentalised by sub-disciplines. This article proposes a heuristic framework to map the effects of war in terms of ripple and backwash across a range of interconnected layers of societies. Through this framework, the article then introduces a set of empirically rich and theoretically informed studies from across multiple disciplines which examine the first consequences of the war in Ukraine. Taken together, these studies show that the war has had deep and complex effects affecting human life; human development; economies; values and attitudes; policy and governance; and power distribution and relations around the world. Although broader international public interest in the war may have waned within weeks of the invasion, the effects of the conflict have been deep and continued in many areas, but also differentiated across space and time. Traditional public policy concepts used to frame the effects of “external shocks” such as punctuated equilibrium and critical junctures may overlook such deep-seated and diverse effects, warranting the multidisciplinary lenses used in this volume.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".