Teaching Professional Ethics to Educators: Assessing the “Multiple Ethical Languages” Approach
Bibliographic record
Abstract
In his article "Educational Ethics: Are We on the Right Track?" Jerry Coombs raises critical questions about two standard approaches to teaching professional ethics to educators, then proposes alternative ways in which such courses can help teachers and administrators enhance their capacity for sound practical judgment, that is, make the right decisions when faced with challenging moral situations in their practice.1 In this essay, I raise critical questions about a third way of designing professional ethics courses that has recently become popular, the "multiple ethical languages" approach, then introduce a theory and practice of improving practical judgment that I believe warrants exploration.The essay has three sections.In section one, I recap key points from the Coombs article to establish criteria for assessing professional ethics programs, then review how teaching ethical languages has been understood to improve practical judgment.In section two, I examine a text exemplifying the multiple ethical languages approach to show how coaching teachers and administrators in a range of ethical languages is not sufficient to help them improve their decision-making in the complex moral contexts of professional practice.In the third and final section, I propose, taking Robert Nash's reference to "moral discernment" 2 as my cue, that instructors of professional ethics courses should pay greater attention to the intuitive dimension of the theory-practice dialectic through which educational professionals -ourselves not least -might cultivate the conditions for sound practical judgment. COOMBS ON PROFESSIONAL ETHICS FOR EDUCATORSCiting research by James Wallace, Coombs reports that there are two kinds of ethical problems that educators and other professionals find particular vexatious: problems of relevance and problems of conflict.
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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 | 0.046 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".