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Record W2609624560 · doi:10.29173/mruhr334

Chimneys in the Night: A Comparative Analysis of Elie Wiesel’s Night and Olga Lengyel’s Five Chimneys

2017· article· en· W2609624560 on OpenAlexaffvenue
Layla Dawn Leard

Bibliographic record

VenueMount Royal Undergraduate Humanities Review (MRUHR) · 2017
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsMount Royal University
Fundersnot available
KeywordsDepictionMemoirNazi concentration campsThe HolocaustHuman sexualityGender studiesHistorySociologyArt historyArtLiteratureTheologyPhilosophyArchaeology

Abstract

fetched live from OpenAlex

This essay aims to evaluate some of the similarities and differences in the experiences of two Holocaust survivors, Olga Lengyel and Ellie Wiesel. The essay will explore the experiences of these two survivors in Auschwitz, Birkenau, and Buchenwald and examine why some of their experiences may have been different. The purpose of the essay is not to belittle the experiences of one gender or the other, but to identify how gender and sexuality made their experiences different. Wiesel’s and Lengyel's haunting memories of their experiences in these concentration camps offers a lense through which to examine the potential role that gender had on the experiences of the camp inmates. Both authors provide a graphic depiction of life in the concentration camp and the reader is taken into the depths of the hell in which these human beings were forced to live. Lengyel and Wiesel in a sense represent larger groups of people; women in the concentration camp and men in the concentration camp. Their memoirs exemplify the experiences of the millions of men and women who lived in the concentration camps, many of whom’s voices were silenced as a result of their presence in the camps. Therefore one can use the two accounts and the wealth of information within them to draw general conclusions about the experiences of each gender within the camp.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.081
GPT teacher head0.349
Teacher spread0.268 · 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 designTheoretical or conceptual
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
Published2017
Admission routes2
Has abstractyes

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