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Record W4410603517 · doi:10.1080/00085006.2025.2494896

Destroyed temples of Ukraine: religion and culture on the battlefield

2025· article· en· W4410603517 on OpenAlexvenueaboutno aff
Ihor Poshyvailo

Bibliographic record

VenueCanadian Slavonic Papers · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Tourism and Spaces
Canadian institutionsnot available
Fundersnot available
KeywordsBattlefieldAncient historyPolitical scienceHistory

Abstract

fetched live from OpenAlex

In the three years since the outbreak of full-scale war, several thousand Ukrainian cultural and heritage sites have been damaged. Almost a thousand, mostly religious, buildings have been destroyed. This article discusses the emergency preparedness and response of the cultural sector to the war in Ukraine. In the face of large-scale war, the Ukrainian cultural sector quickly organized itself to protect collections, venues, and people through non-governmental organizations (including the Heritage Emergency Response Initiative, launched in March 2022). Among the leading partners in this alliance has been the Maidan Museum in Kyiv. To raise awareness about the war’s devastating nature and to tell the emotional stories of the cultural ecosystem, the Maidan Museum and its partners produced the exhibit Destroyed Temples of Ukraine, which is currently on display in Ukraine and abroad. The exhibit was unveiled in Edmonton in March 2024 and has since been touring Canada with the support of the University of Alberta and the Canadian Institute of Ukrainian Studies. This report introduces the exhibit, its genesis, and some of its contents.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.653

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.005
Scholarly communication0.0040.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.007
GPT teacher head0.254
Teacher spread0.247 · 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 designObservational
Domainnot available
GenreEmpirical

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 routes2
Has abstractyes

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