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Record W2055959411 · doi:10.1159/000272889

Aging as Ripening: Character and Consistency of Moral Judgment in Young, Mature, and Older Adults

2009· article· en· W2055959411 on OpenAlexaff
Michael W. Pratt, Gail Golding, William J. Hunter

Bibliographic record

VenueHuman Development · 2009
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsLawrence Kohlberg's stages of moral developmentPsychologyConsistency (knowledge bases)Maturity (psychological)Moral developmentSocial psychologyDevelopmental psychologyTest (biology)Character (mathematics)Defining Issues Test

Abstract

fetched live from OpenAlex

The hypothesis that with maturity moral judgments would become more organized and consistent was studied over the adult life span. 10 men and 10 women in each of three age groups (18–24, 30–50, and 60–75) were interviewed. Both stage levels and philosophical orientations of moral thinking were assessed. Three different tasks were used: Kohlberg’s Moral Judgment Interview, Rest’s Defining Issues Test, and a Story Pair task developed for this study, focused on usage of ‘fairness’ or ‘utilitarian’ philosophical orientation. Results showed few differences in average stage level or orientation among the three age groups. However, consistency between moral stages produced and those preferred by the subject increased markedly with age. So did the consistency of moral orientation usage. These results support a hypothesis of increasing philosophical reflectiveness with maturity.

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)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.054
GPT teacher head0.275
Teacher spread0.221 · 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

Citations40
Published2009
Admission routes1
Has abstractyes

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