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Record W2137319396 · doi:10.1093/geront/gnu168

Holocaust Survivors’ Memories of Past Trauma and the Functions of Reminiscence

2015· article· en· W2137319396 on OpenAlexafffund
Norm O’Rourke, Sarah L. Canham, Annette Wertman, Habib Chaudhury, Sara Carmel, Yaacov G. Bachner, Hagit Peres

Bibliographic record

VenueThe Gerontologist · 2015
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsReminiscenceGenerativityGratitudePsychologyAutobiographical memoryProsocial behaviorNarrativeThe HolocaustRecallLife reviewDevelopmental psychologySocial psychologyCognitive psychologyMedicineArt

Abstract

fetched live from OpenAlex

PURPOSE OF THE STUDY: Existing research suggests that specific ways of recalling autobiographical memories of one's past cluster in self-positive, self-negative, and prosocial reminiscence functions. We undertook the present qualitative study to gain understanding of reminiscence functions as described by 269 Israeli Holocaust survivors and to see whether groupings of themes that emerged would correspond to our tripartite model of the reminiscence functions. DESIGN AND METHODS: Participants (M = 80.4 years; SD = 6.87) were asked to describe memories that typify a reminiscence function in which they frequently or very frequently engage. Thematic analyses were conducted in English (translated) and Hebrew. RESULTS: Responses reflect the range of ways in which Holocaust survivors reminisce. The task of describing early life memories was difficult for some participants, while others' lived experiences enabled them to teach others to ensure that their collective memory remains in the consciousness of the next generation of Israelis and the Jewish state. Data are imbued with examples of horror, resilience, generativity, and gratitude. IMPLICATIONS: As hypothesized, survivors' memories cluster in self-positive, self-negative, and prosocial groupings consistent with the tripartite model of reminiscence functions.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.925

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
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 head0.079
GPT teacher head0.330
Teacher spread0.250 · 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 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

Citations31
Published2015
Admission routes2
Has abstractyes

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