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Living in History

2009· article· en· W2110290963 on OpenAlexafffund
Norman Brown, Peter J. Lee, Mirna Krslak, Frederick G. Conrad, Tia G. B. Hansen, J Havelka, John R. Reddon

Bibliographic record

VenuePsychological Science · 2009
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCollective memoryTerrorismPsychologyIdentity (music)Social psychologyHolocaust survivorsPopulationSpanish Civil WarCollective identityAutobiographical memorySociologyPolitical scienceLawThe HolocaustDemographyAestheticsPsychiatry

Abstract

fetched live from OpenAlex

Memories of war, terrorism, and natural disaster play a critical role in the construction of group identity and the persistence of group conflict. Here, we argue that personal memory and knowledge of the collective past become entwined only when public events have a direct, forceful, and prolonged impact on a population. Support for this position comes from a cross-national study in which participants thought aloud as they dated mundane autobiographical events. We found that Bosnians often mentioned their civil war and that Izmit Turks made frequent reference to the 1999 earthquake in their country. In contrast, public events were rarely mentioned by Serbs, Montenegrins, Ankara Turks, Canadians, Danes, or Israelis. Surprisingly, historical references were absent from (post-September 11) protocols collected in New York City and elsewhere in the United States. Taken together, these findings indicate that it is personal significance, not historical importance, that determines whether public events play a role in organizing autobiographical memory.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.059
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0590.005

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 head0.093
GPT teacher head0.384
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations101
Published2009
Admission routes2
Has abstractyes

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