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Record W4415678385 · doi:10.1080/00221325.2025.2579908

Moral Prioritization and Consistency in Adolescents’ Moral Decision-Making: The Role of Moral Identity Abstraction

2025· article· en· W4415678385 on OpenAlexafffund
Zachariah I. Hamzagic, Tobias Krettenauer, Jean Paul Lefebvre

Bibliographic record

VenueThe Journal of Genetic Psychology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsMcMaster UniversityWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMoral disengagementSocial cognitive theory of moralityConstrual level theoryIdentity (music)Consistency (knowledge bases)Moral reasoningMoral psychologyMoral development

Abstract

fetched live from OpenAlex

Moral identity predicts moral decision-making and behavior. Moral identity goal theory claims that moral identity becomes more abstract with age. A concrete construal of moral identity refers to thinking of temporally more recent context specific moral actions, whereas an abstract construal of moral identity encompasses temporally more distant and generalized moral values. Construal level has been shown to be related to decision-making in a variety of areas (e.g. health) including the moral domain. In this study, we collected data from 246 early adolescents (Mage =13.32, 123 females) and 250 young adult university students (Mage =19.94, 181 females) about moral identity and decision-making in hypothetical scenarios. We found that moral identity was more abstract in the older age group. Moreover, moral identity abstraction predicted the prioritization of moral concerns over conflicting self-interest as well as greater consistency in moral decision-making across situations. The findings provide empirical support for moral identity abstraction as an important aspect of moral identity development.

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 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 score0.146
Threshold uncertainty score0.270

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 head0.049
GPT teacher head0.334
Teacher spread0.285 · 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 teacher head, 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

Citations1
Published2025
Admission routes2
Has abstractyes

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