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Record W4297004103 · doi:10.33137/ijournal.v7i3.39326

Teaching Trauma Without Traumatizing

2022· article· en· W4297004103 on OpenAlexaffvenue
Rebecca Ford

Bibliographic record

VenueThe iJournal Student Journal of the Faculty of Information · 2022
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsThe HolocaustTriad (sociology)ExhibitionAgency (philosophy)Task (project management)Set (abstract data type)PsychologyElement (criminal law)Scope (computer science)Visual artsComputer sciencePsychoanalysisSociologyArtEngineeringPolitical science

Abstract

fetched live from OpenAlex

Holocaust education is as vital as it is global in scope. However, museums cannot simply present the uninterpreted, vivid trauma of the Holocaust to young children. This challenge is then further complicated as museums must make the learning enjoyable enough that children want to learn it—a seemingly impossible task. To avoid Holocaust fatigue or traumatizing young audiences, I suggest here a triad model with which museum professionals should engage to create a meaningful and educational Holocaust exhibit for children. The triad model is based on participant agency, authenticity, and personal connection and can be seen in examples worldwide. In this article, I will focus on three main exemplars and specifically outline how they include each element of the proposed triad conceptual model in their exhibitions. To further illustrate its importance, I will close with a brief direct comparison to one recent Holocaust exhibit that does not engage fully with all the elements of the triad model and thus is a missed opportunity for youth participants.

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)0.004
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 score0.485
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.042
GPT teacher head0.348
Teacher spread0.307 · 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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