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Record W6983415061

Mediating memory through materiality: trauma iconography of flight and expulsion in the 21st-century museum

2018· dissertation· en· W6983415061 on OpenAlexfundno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2018
Typedissertation
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaDeutscher Akademischer AustauschdienstUniversity of Manitoba
KeywordsIconographyNational identityGermanCollective memoryExhibitionObject (grammar)MnemonicRepresentation (politics)MetaphorEthnomethodologyBroadside
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines how German and Polish museums employ the social meanings rooted in flight and expulsion visual trauma iconography in their individual narrative, experiential, and spatial structures through the process of Inszenierung (staging). It does so to better understand the ways in which these museums interact with and intervene in national and transnational memory discourses surrounding flight and expulsion. The Landesmuseen and travelling exhibitions under analysis work towards the construction of an ethno-regional diasporic identity for the expellee community, rooted in nostalgia and collective victimhood. Trauma iconography is used to underscore flight and expulsion as the ultimate historical cataclysm. The national and transnational museums under examination only nominally include flight and expulsion in their overall structures. Despite this, they interrupt or reinterpret the previously culturalized content of these trauma icons in order to create cognitive dissonance between the visitor and more traditional mnemonic patterns of flight and expulsion.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
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 studies0.0000.000
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 head0.025
GPT teacher head0.257
Teacher spread0.231 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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