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Resilience and Positive Emotions: Examining the Role of Emotional Memories

2008· article· en· W2146211157 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

VenueJournal of Personality · 2008
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsPsychologySadnessPsychological resilienceContext (archaeology)MoodTraitNegative affectivityAnxietyPositive affectivityAutobiographical memoryDevelopmental psychologyPersonalitySocial psychologyCognitive psychologyAngerRecall

Abstract

fetched live from OpenAlex

Resilience has been frequently associated with positive emotions, especially when experienced during taxing events. However, the psychological processes that might allow resilient individuals to self-generate those positive emotions have been mostly overlooked. In line with recent advances in memory research, we propose that emotional memories play an important role in the self-generation of positive emotions. The present research examined this hypothesis in two studies. Study 1 provided initial data on the validity and reliability of a measure of emotional memories networks (EMN) and showed that it had a predictive value for broad emotion regulation constructs and outcomes. In addition, Study 1 showed that positive EMN mediated the relationship between psychological resilience and the experience of positive emotions in a context of sadness, even after controlling for pre-experimental positive mood. Study 2 replicated results of Study 1 in a context of anxiety and after controlling for positive affectivity trait.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.209

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.362
Teacher spread0.319 · 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