Ethical Decision Making: Social Metaphors Towards Ethical Action
Bibliographic record
Abstract
The idea that ethical practice lays within the individual has tended to hold predominance in the fields of social, education, and health care (Hugman, 2005; Kinsella, 2005; Shaw, 2011). A more contextual-based understanding of ethical decision making is presented. Central to understanding how practitioners might begin to approach their practice ethically is the idea that decision making is a social meaning-making activity, and takes place within context. We propose that in constructing ethics with others through a heightened recognition of contextual complexity, we have the potential to develop ethically aware practice that begins to address the inevitable uncertainties in complex situations. Four interrelated metaphors, Map, Moment, Meta, and Mind, which emerge from systemic principles and can be represented as poles of two spectra, are presented. These are considered as part of a practice model and a useful way of relating to the process of ethical decision-making.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.077 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".