Bibliographic record
Abstract
Ohannes Geukjian's new book is a brilliant contribution, shedding light on Russia's actions in Syria, the Middle East and the broader world. The arguments share existing hypotheses, but the originality of the book relies on theoretical frameworks borrowed from social psychology and social anthropology ‘to explain and analyse Russia's seemingly irrational and unpredictable behaviour’ (p. 14). As Geukjian explains, Russia's behaviour in Syria (and elsewhere, namely during the annexation of Crimea in 2014) can be best understood through the lens of a status-seeking strategy. This approach differentiates this book from other contributions on Russia's intervention in the Syrian war, including Anna Borshchevskaya's Putin's war in Syria: Russian foreign policy and the price of America's absence (London: I. B. Tauris, 2021; reviewed in International Affairs 98: 3, May 2022) and Roy Allison's article ‘Russia and Syria: explaining alignment with a regime in crisis’ (International Affairs 89: 4, July 2013) among others.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".