The necessity of a relational ethics alongside Noddings’ ethics of care in narrative inquiry
Bibliographic record
Abstract
Amidst a winter snow storm we drove slowly and carefully to our research site. Leaving much earlier than usual we wanted to be there to greet the indigenous youth who we had come to know in the process of inquiring into their ongoing identity making. We came to know them over several months in a junior high school arts club and had developed relationships with them that were marked by care. In attending to care, Noddings (1984) offered us a way to think about ethics. Yet Noddings did not explicitly turn her attention to an ethics for research, rather her focus was on an ethics of care in moral education. Drawing on our work alongside indigenous youth we show how these four components of an ethics of care shaped our narrative inquiry and show how a relational ethics builds on, and extends, an ethics of care in narrative inquiry.
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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 | 0.060 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.105 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| 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".