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Record W3107529249 · doi:10.32927/zzsim.532

Krzesiny i Kreising – między pamiętaniem a pomijaniem. Polskie miasteczko wobec historii, pamięci i rywalizacji w cierpieniu

2014· article· en· W3107529249 on OpenAlexaff
Danijel Matijevic, Jan Kwiatkowski

Bibliographic record

VenueZagłada Żydów Studia i Materiały · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicPolish-Jewish Holocaust Memory Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsCollective memoryPhenomenonHistoryPopulationSociologyLawPolitical sciencePhilosophyDemographyEpistemology

Abstract

fetched live from OpenAlex

The area around Krzesiny, located near the city of Poznań, Poland, witnessed several dark events during World War II: Germans oppressed the local population, culminating in a terrorizing action dubbed “akcja krzesińska;” also, a forced labor camp, named “Kreising,” was built near the township, housing mainly Jews. After the war, the suffering in Krzesiny was remembered, but selectively – “akcja” and other forms of Polish suffering were commemorated, while the camp was not. By exploring the “lieux de mémoire” in Krzesiny – dynamics of memory in a small township in Poland – this paper uses localized research to address the issue of gaps in collective memory and commemoration. We briefly look at the relevant history, Polish memory regarding wartime events in Krzesiny, and the postwar dynamics of collective memory. Discussing the latter, we identify a new phenomenon at work, one which we dub “collective disregard” – group neglect of the past of the “Other” that occurs without clear intent. We argue that “collective disregard” is an issue that naturally occurs in the dynamics of memory. By making a deliberate investment in balanced remembrance and commemoration, societies can counter the tendencies of “disregard” and curb the controversies of competitive victimization claims, also called “competitive martyrdom”.

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), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.203
Teacher spread0.182 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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