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Record W2100523738 · doi:10.1177/0164027510364122

Mapping the Future of Reminiscence: A Conceptual Guide for Research and Practice

2010· article· en· W2100523738 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch on Aging · 2010
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsLangara College
Fundersnot available
KeywordsReminiscenceField (mathematics)Conceptual modelPsychologyHeuristicComponent (thermodynamics)Management scienceEngineering ethicsComputer scienceData scienceEpistemologyCognitive scienceCognitive psychologySociologyArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Nearly 50 years after Butler’s seminal 1963 contribution, the field of reminiscence and life review is entering a more mature stage. Isolated examples of increasingly sophisticated studies have recently emerged that can serve as a sound, cumulative data base. However, the field lacks an overarching conceptual model describing emerging trends, neglected domains, and key linkages among component parts. In the present article, the authors selectively, yet critically, review prior limitations and promising developments and then describe a comprehensive, multifaceted conceptual model that can guide future research and practice. The authors initially situate their model within a particular theoretical orientation (i.e., life-span psychology). They then describe a heuristic model that identifies and discusses triggers, modes, contexts, moderators, functions, and outcomes. Finally, the authors illustrate how these interactive factors influence both theoretical and applied areas.

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.

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)0.018
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.307
GPT teacher head0.560
Teacher spread0.252 · 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