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Record W2614976182 · doi:10.1037/dev0000353

Why be moral? Moral identity motivation and age.

2017· article· en· W2614976182 on OpenAlexafffund
Tobias Krettenauer, Rosemary Victor

Bibliographic record

VenueDevelopmental Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyMoral developmentIdentity (music)Developmental psychologyMoral disengagementConstruct (python library)Social psychologySocial cognitive theory of moralityPsycINFOPersonal identityIdentity formationSelf-concept

Abstract

fetched live from OpenAlex

Moral identity research to date has largely failed to provide evidence for developmental trends in moral identity, presumably because of restrictions in the age range of studies and the use of moral identity measures that are insensitive to age-related change. The present study investigated moral identity motivation across a broad age range (14-65 years, M = 33.48; N = 252) using a modified version of the Good Self-Assessment Interview. Individuals' moral identity motivation was coded and categorized as external, internal, or relationship-oriented. It was found that with age, external moral identity motivation decreased, whereas internal moral identity motivation increased. Effects of age were stronger in adolescence and emerging adulthood than in young adulthood and middle age. Findings underscore the developmental nature of the moral identity construct and suggest that moral motivation becomes more self-integrated with age. (PsycINFO Database Record

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)0.011
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.117
GPT teacher head0.391
Teacher spread0.274 · 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

Citations70
Published2017
Admission routes2
Has abstractyes

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