Eye of the Heart: Knowing the Human Good in the Euthanasia Debate
Bibliographic record
Abstract
What is the role of feelings in the euthanasia debate? This is the central question in William F. Sullivan's unique philosophical and ethical exploration of the issue, Eye of the Heart. Employing the principles and techniques of the great Canadian theologian and thinker Bernard Lonergan, Sullivan offers a concrete examination of the role of feelings in grasping moral values and the key role that feelings play in ethical decision-making. The heart has its reasons, he argues convincingly, and it is a type of reason that bioethicists, philosophers, and legal scholars all need to know. Sullivan draws on his experiences as a practicing physician to analyse the distinguishing elements of human knowing, illustrating them through common examples of decision-making in health care. He highlights the occurrence of various types of insight, particularly 'deliberative insights' that occur in the process of making value judgments. These deliberative insights are affective, and through them, a person apprehends moral values. Eye of the Heart proposes that feelings are relevant to knowing moral values and orient us towards moral self-transcendence. The implications of this stance in ethics are drawn out for the euthanasia debate.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".