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Record W4405596429 · doi:10.7829/jj.21341699

Monuments and Territory

2024· book· en· W4405596429 on OpenAlexfundno aff
Mischa Gabowitsch, Mykola Homanyuk

Bibliographic record

VenueCentral European University Press eBooks · 2024
Typebook
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsnot available
FundersUniversity of TorontoUniversità degli Studi dell'InsubriaAustrian Science FundUniversität WienGerda Henkel Foundation
KeywordsGeography

Abstract

fetched live from OpenAlex

From the very first days of their large-scale attack on Ukraine in February 2022, the Russian invaders have made exceptional efforts to interact with the war memorial landscape of the newly occupied territories. This landscape consists of tens of thousands of monuments, mostly in small towns and villages, commemorating the Second World War and other conflicts, including Ukraine’s resistance against Russia since 2014. The Russians have destroyed some of these memorials, renovated others, and built new monuments amid continued fighting. They also used war memorials in countless propaganda photos and videos aimed for a domestic audience and largely escaping Western attention. Why this fervor? Gabowitsch and Homanyuk draw on unique sources to trace the logic of Russian monument policies in occupied Ukraine. Mykola Homanyuk spent several months in occupied Kherson and collected sources on the ground, often at considerable risk to himself. This exceptional wartime on-site ethnography was complemented by systematic real-time data collection from online sources, many of which have since disappeared. The book shows how Russian invaders believed their own propaganda about Soviet war memorials being mistreated in Ukraine, and what they did when they discovered well-maintained monuments on the ground. More generally, it also discusses the link between monuments and territorial claims by irredentist empires.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.480
Threshold uncertainty score0.891

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.204
Teacher spread0.190 · 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 teacher head, 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

Citations2
Published2024
Admission routes1
Has abstractyes

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