MétaCan
Menu
Back to cohort
Record W4414729891 · doi:10.1007/978-3-031-98724-3_12

The Russia-Ukraine War or Peace?

2025· book-chapter· en· W4414729891 on OpenAlexaff
Ivan Katchanovski

Bibliographic record

VenueRethinking political violence · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEuropean and Russian Geopolitical Military Strategies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsUkrainianDelegationPrime ministerTurkishState (computer science)Outcome (game theory)

Abstract

fetched live from OpenAlex

Abstract This chapter examines the likely outcome of the Russia-Ukraine war and whether this war could have been prevented or resolved peacefully. It analyzes a peace deal framework agreement between Ukraine and Russia that was close to being finalized in spring 2022. The analysis of this peace deal framework and reasons for its failure is based on its text and statements in the media and social media by the head of the Ukrainian delegation at the peace talks, Ukrainian delegation members, Ukrainian officials close to Zelensky, then prime minister of Israel, top official of the US State Department, the ex-German chancellor, president of Turkey, the Turkish foreign minister, and current and former US and Russian leaders and senior officials. The analysis shows that defeat of Ukraine is the most likely outcome of the war.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.003

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.

Opus teacher head0.031
GPT teacher head0.308
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRethinking political violenceSame topicEuropean and Russian Geopolitical Military StrategiesFrench-language works237,207