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Record W2109847760 · doi:10.1037/0012-1649.44.1.254

Stories of the young and the old: Personal continuity and narrative identity.

2008· article· en· W2109847760 on OpenAlexafffund
Kate C. McLean

Bibliographic record

VenueDevelopmental Psychology · 2008
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsNarrativePsychologyDevelopmental psychologyIdentity (music)Coherence (philosophical gambling strategy)Autobiographical memoryThematic analysisNarrative identitySelf-conceptPersonal identityEvent (particle physics)Social psychologyQualitative researchCognitive psychologyRecallLinguistics

Abstract

fetched live from OpenAlex

This study examined narrative identity in 2 groups of participants who were younger (ages ranging from late adolescence through young adulthood) and older (over the age of 65 years). Participants completed an extensive interview in which they reported three self-defining memories. Interviews were coded for several characteristics of autobiographical reasoning: self-event connections representing self-stability or self-change, event-event connections, reflective processing, and thematic coherence. Results showed that the older and younger groups were not different in terms of the frequencies of self-event connections or the levels of reflective processing. However, in comparison with the younger group, the older group had more thematic coherence and more stories representing stability, whereas the younger group had more stories representing change. Gender differences also emerged, suggesting that females may have an advantage in the development of narrative identity. Results are discussed in terms of the different ways to represent narrative identity at 2 ends of the life span.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
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 head0.032
GPT teacher head0.337
Teacher spread0.304 · 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 designQualitative
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

Citations210
Published2008
Admission routes2
Has abstractyes

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