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Record W4297004337 · doi:10.33137/ijournal.v7i3.39329

Poetics of Memory on Dictatorships

2022· article· en· W4297004337 on OpenAlexaffvenue
Ana Cristina Villegas

Bibliographic record

VenueThe iJournal Student Journal of the Faculty of Information · 2022
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversity of Toronto
FundersRoyal Anthropological Institute
KeywordsDictatorshipThe HolocaustExhibitionGenocideVernacularLatin AmericansPoeticsResistance (ecology)GlobalizationHuman rightsMuseologyMedia studiesSociologyArtVisual artsPolitical scienceLiteratureLawDemocracyPoetry

Abstract

fetched live from OpenAlex

This research explores how three memorial museums in “Latin America” exhibit trauma and violence from their respective dictatorial regimes. I analyze the impact of globalization with regard to common Holocaust tropes and themes by examining their content and display techniques via their official virtual platforms. The three memorial museums highlighted in this paper are the Memory and Human Rights Museum (MMDH) in Chile; the Ex Centro Clandestino de Detencion, Tortura y Exterminio (ESMA) in Argentina; and the Memorial Museum of Dominican Resistance in the Dominican Republic. I argue that these museums combine Latin American and Holocaust museological techniques and aesthetics in their exhibitions to accomplish two goals: (1) attract an international audience and (2) receive worldwide recognition in universal organizations such as the United Nations (UN) or UNESCO. The efforts implemented by these museums are apparent in their vernacular (the native language spoken in a particular region), display techniques, vision/purpose statements, and their interpretative text.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.330
Teacher spread0.290 · 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 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

Citations1
Published2022
Admission routes2
Has abstractyes

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