Ethical Discernment Points: The Alchemy of Dialogue, Deliberation, and Decisions
Bibliographic record
Abstract
This paper describes research that explores how counsellors cope with ethically challenging situations, which are referred to in this report as Ethical Decision Points. (EDPs). We explore how counsellors navigate and make decisions about these EDPs, and the degree to which dialogue and conversation with others is part of their discernment process. In this qualitative study, counsellors from across Canada responded to an online ethical scenario that contained several EDPs. Several themes are identified in the analysis that allows us to describe a process employed by counsellors in their ethical decision making. Dialogue is identified as part of the decision process but it is not as prevalent as predicted. We conclude that counsellors include dialogue or talking to others as one step in ethical decision making rather than it being the main component of the process. We present some recommendations for ethics education and counselling practice. Resume Ce document decrit de la recherche qui explore comment les conseillers font face a des situations ethiquement difficiles, lesquelles seront nommees « points de decisions ethiques » (PDE) dans le present document. Nous allons explorer comment les conseillers s’orientent autour de ces PDE, prennent des decisions a leur sujet et a quel point le dialogue et la conversation avec les autres font partie de leur processus decisionnel. Dans cette etude qualitative, des conseillers d’un bout a l’autre du Canada ont reagi par l’entremise du web a un scenario ethique qui contenait plusieurs PDE. Plusieurs themes sont identifies dans l’analyse qui nous permet de decrire un processus utilise par des conseillers dans leur processus decisionnel en ce qui concerne l’ethique. Le dialogue est identifie comme faisant partie du processus decisionnel mais n’est pas aussi predominant que prevu. Nous concluons que les conseillers incluent le dialogue ou la discussion comme etant une des etapes du processus de decision ethique plutot que la partie principale du processus. Nous presentons des recommandations pour l’education ethique et les pratiques de conseillance.
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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 | 0.034 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.016 | 0.063 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.008 |
| 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".