University Ethics Courses and Student Self-Capacities: A Quantitative Study
Bibliographic record
Abstract
Moral identity is part of one's self-concept, and an internalized moral identity has been associated with ethical decision-making (Aquino & Reed, 2002; Gu & Neesham, 2014; Rua et al., 2017). Studies of decision-making biases show that people will make biased decisions to maintain congruence with their self-concept (Higgs et al., 2020; Watts et al., 2020). Self-capacities are an individual’s ability to relate to others while regulating intense negative affect and maintaining a solid sense of self through these relationships and changing emotions (Briere, 1992, 1996). Based on literature suggesting connections between moral identity, ethical decision-making, and self-capacities, in this study I used an online survey data collection method and a series of MANOVAs to examine the potential effect of ethics education on moral identity, decision-making biases, and disrupted self-capacities. The final sample consisted of 158 graduate and undergraduate University of Calgary students from a range of disciplines, 47 of whom had previously studied ethics and 111 who had not. Although none of the MANOVAs yielded significant results, I found that students who had studied ethics showed significantly lower scores on one of the scales of the instrument used to measure self-capacities, Susceptibility to Influence. Additionally, post hoc exploration of Pearson correlations among the instrument scales indicated several significant relationships between the disrupted self-capacities scales and decision-making bias scales for students who had studied ethics. Possible implications for counselling and ethics education are discussed. Keywords: Self-capacities, moral identity, decision-making biases, ethics education
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".