Institutionalizing an “Ethic of Care” into the Teaching of Ethics for Pre-service Teachers
Bibliographic record
Abstract
This paper calls for the acknowledgement and institutionalization of an ethic of care into the education of decision-making processes for pre-service teachers. The impetus for this paper came from the author's experiences with teaching a mandatory ethics and law course for pre-service teachers. Over the course of their teaching and as expounded upon in this paper, the authors illustrate how the course goals, aims, objectives and readings ignore discussions on gender in the teaching profession. Using a critical feminist policy analysis, the authors analyse the ethical perspectives taught in the required textbooks. Findings suggest that the absence of the “ethic of care” perpetuates a gender regime and teaching as “women’s work” while ignoring ethical perspectives founded outside of the rational male perspectives. This notion of mandating an ethic of care into the teaching of ethics for pre-service teachers is our attempt to address issues of power and privilege by pointing to a gap in the curriculum of university ethics courses.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.059 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.009 |
| 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".