Moral Identity: From Theory to Research
Bibliographic record
Abstract
Moral identity, often defined as the importance or centrality of moral values to a person’s sense of self, has long been understood to play an important role in moral functioning. However, critical gaps remain regarding its development, cross-context stability, and behavioural significance. This dissertation addresses these gaps in three empirical studies, providing new insights into the nature and function of moral identity across the lifespan and in daily life. The first study (Chapter 2) explores developmental trends in moral identity, testing predictions from moral identity goal theory (Krettenauer, 2022). Using a cross-sectional sample spanning adolescence to old age, the study finds that with age moral identity becomes increasingly informed by abstractly rather than concretely construed values, and increasingly underwritten by internal rather than external motivation. The second study (Chapter 3) examines the stability and malleability of moral identity using experience sampling methods (ESM). By tracking momentary fluctuations in moral identity salience in a sample of Canadian university students over the course of a week, the study demonstrates that moral identity varies significantly within individuals across contexts while also showing stable between-person differences. Further, it shows that within- and between-person differences are related to a variety of morally relevant events experienced in everyday life. The third study (Chapter 4) also employs ESM to explore how moral and immoral action undertaken in daily life can be independently predicted by both within-person fluctuations and between-person differences in the salience and motivation of moral identity. It finds that while variation at both of these levels in moral identity is predictive of discrete actions and behavioural dispositions, this relation is more consistently found and stronger at the within-person level. Moral identity motivations are also found to be uniquely predictive of behaviour, in context-dependent ways. Together, these studies provide a comprehensive view of moral identity as a dynamic construct that develops over the lifespan, exhibits trait-like stability while also responding to situational factors, and profoundly influences behavior. By bridging developmental, socio-cognitive, and individual difference perspectives, this research contributes to a more nuanced understanding of moral identity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.046 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".