Adolescents’ moral reasoning when honesty and loyalty collide
Bibliographic record
Abstract
Abstract According to moral pluralism theory, people practice moral reasoning based on several fundamental dimensions, including honesty and loyalty. As individuals navigate increasingly complex social worlds over development, they may face the dilemmas where honesty collides with loyalty. In the current study, adolescents (15‐ to 18‐year‐olds, N = 203) in a western, multicultural Canadian city read moral dilemmas involving a protagonist learning that an athlete cheated in a sports event. We manipulated the relationship between the protagonist and the cheater (best friends or compatriots) between subjects and the protagonist's responses (telling a loyal lie or the disloyal truth) within subjects. We examined participants’ first‐person behavioral intentions (choices) in the hypothetical dilemmas and third‐party judgments of protagonists’ morality. These adolescents projected that they personally would be more inclined to tell a loyal lie for a friend than their country, but older adolescents were more likely to lie for their country than younger ones. Participants judged telling disloyal truths to expose a friend significantly less favorably than disloyal truths to expose a country. These adolescents reflected upon loyalty and caring, honesty and fairness, and nonmoral practical factors when justifying their choices and judgments. The current study advances our understanding of moral development by revealing that with sophisticated social‐cognitive capacities, adolescents can coordinate different fundamental moral values when rendering their moral reasoning.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".