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Record W3134165108 · doi:10.1080/14782804.2021.1891869

Mobility, Multiculturalism and Memory in Croatian Istria

2021· article· en· W3134165108 on OpenAlexaff
Renata Schellenberg

Bibliographic record

VenueJournal of Contemporary European Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicBalkans: History, Politics, Society
Canadian institutionsMount Allison University
Fundersnot available
KeywordsMulticulturalismIdentity (music)HybridityPoliticsCultural identityGeopoliticsEthnic groupHomelandSociologyGender studiesState (computer science)Political scienceAnthropologyLawAestheticsSocial scienceArt

Abstract

fetched live from OpenAlex

This article examines Istrianism as a form of regional cultural identity. In doing so, it regards Istria as an important transnational borderland and investigates the historical circumstances that underlie the manifest multiculturalism of the region. It focuses on an analysis of key socio-historical circumstances that conditioned the cultural heterogeneity of the region, exploring the impact Habsburg heritage in particular had on current socio-cultural policies and interactions. It examines the Istrian peninsula as part of the Austrian Riviera to determine the effects the polyphonic state structure of the Austro-Hungarian Empire had, probing whether this multicultural legacy is connected to the sustained cultural hybridity of Istria today. The present-day IDS party is examined in terms of its relationship to this past. This paper posits the Austrian Littoral as a tolerant, multi-ethnic space where notions of belonging and cultural identity became purposely intertwined, producing a distinct form of citizenship that was not defined by political ordinance alone, but rather by human agency and the immediacy of basic day to day interactions.

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)0.001
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.009
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.001
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 head0.104
GPT teacher head0.352
Teacher spread0.248 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueJournal of Contemporary European StudiesSame topicBalkans: History, Politics, SocietyFrench-language works237,207