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Record W758804972 · doi:10.5206/uwoja.v20i1.8921

Museums Narrating the Nation: Case Studies from Greece and Bosnia-Herzegovina

2012· article· en· W758804972 on OpenAlexaff
Evan Taylor

Bibliographic record

VenueThe University of Western Ontario Journal of Anthropology · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNarrativePluralism (philosophy)National identityEthnic groupIdentity (music)MuseologyBosnia herzegovinaSociologyHistoryPolitical scienceMedia studiesGender studiesAnthropologyAestheticsEthnologyLawArtLiteratureArchaeology

Abstract

fetched live from OpenAlex

A museum that represents a community’s history and culture has the ability to influence the way that visitors understand that community in the present. In this paper, museums in Greece and Bosnia-Herzegovina are examined as case studies in order to better understand how museums attempt to narrate national identity to visitors, both domestic and international. Critical analysis of exhibits in these museums reveal that museum narratives often attempt to project the image of singular national identities. Meanwhile, they may deny the history of place of other contemporary or historic communities that are held in disfavor by those who influence the development of exhibits. In Greece, museums project a ‘Greek’ identity based on Classical, Byzantine, and post-Ottoman history. Museums in Bosnia-Herzegovina emphasize a unifying, shared history of the state’s three main ethnic communities without recognizing the profound differences felt between these communities today. Thus, these institutions may be seen as attempts at encouraging visitors to imagine the nation in one way only, without recognizing pluralism. While these case studies do not necessarily represent a universal trend, they demonstrate the need to reflect upon the place of such museums in contemporary society.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.151
GPT teacher head0.260
Teacher spread0.109 · 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.

Study designQualitative
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

Citations2
Published2012
Admission routes1
Has abstractyes

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