Moral Identity and the Acquisition of Virtue: A Self-Regulation View
Bibliographic record
Abstract
The acquisition of virtue can be conceptualized as a self-regulatory process in which deliberate practice results in increasingly higher levels of skillfulness in leading a virtuous life. This conceptualization resonates with philosophical virtue theories as much as it converges with psychological models about skill development, expertise, goal motivation, and self-regulation. Yet, the conceptualization of virtue as skill acquisition poses the crucial question of motivation: What motivates individuals to self-improve over time so that they can learn from past experience, correct mistakes, and expand their ethical knowledge to new and unfamiliar circumstances? In this paper, it is argued that the motivation to increase one’s level of skillfulness in leading a virtuous life is supported by a specific identity goal, namely the goal to be a moral person. However, this moral identity goal needs to carry specific goal characteristics in order to effectively provide this motivation. It needs to be sufficiently abstract, internally motivated and promotion- rather than prevention-oriented. Research in developmental psychology suggests that the moral identity of children is rather concrete, externally motivated, and prevention-oriented. With development, higher levels of abstraction, internal motivation, and promotion-orientation gain importance providing an important motivational basis for a self-regulated process of virtue acquisition.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".