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Record W3169036453 · doi:10.1111/bjso.12476

The malleability of collective memories: One year after the Tulip Revolution in Kyrgyzstan

2021· article· en· W3169036453 on OpenAlexaff
Mathieu Caron‐Diotte, Roxane de la Sablonnière, Nazgul Sadykova

Bibliographic record

VenueBritish Journal of Social Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyCollective memoryMalleabilitySocial psychologySocial changeGroup (periodic table)Collective behaviorSocial groupDevelopmental psychologyPolitical scienceSociologySocial scienceLawComputer security

Abstract

fetched live from OpenAlex

How people view their social groups' history has important implications at both the collective and the individual levels. It has been established that collective memories, the representations of one's groups' history, differ between generations and individuals. Yet, it remains unclear how collective memories change in reaction to dramatic social change. Using temporal collective relative deprivation (TCRD), which reflects how individuals perceive their group's situation in history, we hypothesize that TCRD trajectories change over time and that highly identified individuals will be less likely to change their trajectory. Kyrgyz nationals (N = 166) responded immediately after the Tulip Revolution and one year later to TCRD measurements. Dual group-based trajectory analysis indicates that a third of the sample modified their memories about their group's situation one year after a dramatic social change and that low identification with the group predicted this change. These results support the idea that collective memory is revised at the individual level after dramatic social change.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.334
Teacher spread0.311 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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