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Record W2770902207 · doi:10.1027/1016-9040/a000302

Wisdom and How to Cultivate It

2017· article· en· W2770902207 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

VenueEuropean Psychologist · 2017
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsHumilityScholarshipPsychologySituational ethicsSet (abstract data type)Context (archaeology)EpistemologyNarrativeTransformative learningEmpirical researchSocial psychologySociologyPedagogy

Abstract

fetched live from OpenAlex

Abstract. Some folk beliefs characterize wisdom as an essence – a set of immutable characteristics, developing as a consequence of an innate potential and extraordinary life experiences. Emerging empirical scholarship involving experiments, diary, and cross-cultural studies contradicts such folk beliefs. Characteristics of wise thinking, which include intellectual humility, recognition of uncertainty and change, consideration of different perspectives, and integration of these perspectives, is highly variable across situations. Cumulatively, empirical research suggests that variability in wise thinking is systematic, with greater wisdom in ecological and experimentally-induced contexts promoting an ego-decentered (vs. egocentric) viewpoint. Moreover, teaching for wisdom benefits from appreciation of context-dependency of intentions and actions depicted in the narratives of wisdom exemplars’ lives. I conclude by advancing a constructivist model of wisdom, suggesting that cultural-historical, personal-motivational, and situational contexts play a critical role for wisdom, its development, and its application in daily life.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.625
Threshold uncertainty score0.996

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.136
GPT teacher head0.443
Teacher spread0.306 · 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