Bernard Lonergan’s cognitive theory and Aristotlian [sic] phronesis : toward a conception of performative objectivity in virtue ethics
Bibliographic record
Abstract
Virtue ethics bases moral decision-making, in part, in a skills-like ability to spot morally salient features in the phenomenal world. Aristotle, an exemplar of such ethics, developed a theory of practical wisdom (phronesis) wherein the person of moral expertise makes sound ethical judgments and decisions via direct insight into concrete circumstances. Phronesis involves emotive discernment and agent-relative construal of the world. But emotions and perspectives can also be linked with bias and poor judgment. How are we to think about objectivity if virtue ethics relies upon such apparently subjective factors as emotively influenced dispositions? Bernard Lonergan developed a theory of insight-based human understanding and explores its operation in the concrete circumstances of practical living. His theory offers a process of human learning and knowing that does not avoid emotions and agent-perspectives but seeks to so operate within these factors as to produce a state we could validly call 'being objective.' Agreeing with Aristotle on a key role for emotions, Lonergan goes much further in claiming that some mode of the emotion of love should play a critical role in attaining the insights required for virtue ethics. His treatment of love, however, remains vague. Martha Nussbaum, on the other hand, provides very rich treatment of how love, understood as compassion, can act as a control for perspectival discernment and moral insight. Her theory fills out Lonergan's, allowing us more clearly to see how love (and other emotions) could be conducive to fostering objective viewpoints within morally charged circumstances. Objectivity in virtue ethics can be understood, then, to be what we are more or less attaining to the degree that we self-consciously apprehend and deploy our innate modes of insight-induction and aim these cognitive processes toward a world construed by a background concern of care and compassion.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| 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".