Bibliographic record
Abstract
Abstract. A key goal for a professional ethics teacher is to help students improve their moralreasoning within the context of their profession, with the ultimate aim of developing a commitmentto the values of their future profession. Using Rest’s Four Component Model as a framework, thisstudy examines the relationship between the first two components of moral sensitivity and moraljudgment. The study utilises two sc ores from the same cohort of co mputing undergraduates: a scorefor ethical sensitivity using a devised dilemma analysis; and a score for change in moral judgmentresulting from an educational inte rvention, using the Defining Issu es Test (DIT). Although averageDIT scores showed no significant improvement in moral judgment, this study found that levels ofethical sensitivity had a significant impact on the development of moral judgment. The paperprovides evidence that ethical sensitivity appears to play a key role in the development of moraljudgment. Therefore an initial key objective critical to any ethics course should be to raise studentlevels of ethical sensitivity as a necessary foundation for developm ent of moral judgment. The paperalso highlights the wide range of levels of ethical sensitivity measured within one cohort andsuggests targeted learning support should be provided to students who score in the lower part of thescale to raise their levels of moral sensitivity early in the course.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.976 | 0.969 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".