A Framework for Authentic Ethical Decision Making in the Face of Grand Challenges: A Lonerganian Gradation
Bibliographic record
Abstract
This paper contributes to the contemporary business ethics narrative by proposing an approach to corporate ethical decision making (EDM) which serves as an alternative to the imposition of codes and standards to address the ethical consequences of grand challenges, like COVID-19, which are impacting today's society. Our alternative approach to EDM embraces the concept of reflexive thinking and ethical consciousness among the individual agents who collectively are the corporation and who make ethical decisions, often in isolation, removed from the collocated corporate setting. We draw on the teachings of the Canadian philosopher and theologian, Fr. Bernard Lonergan, to conceptualize an approach to EDM which focuses on the ethics of the corporate agent by nurturing the universal and invariant structure that is operational in all human beings. Embracing Lonergan's dynamic cognitive structure of human knowing, and the structure of the human good, we advance a paradigm of EDM in business which emboldens authentic ethical thought, decision making, and action commensurate with virtuous living and germane to human flourishing. Lonergan's philosophy guides us away from the imposition of over-arching corporate codes of ethics and inspires us, as individual agents, to attend to the data of our own consciousness in our ethical decision making. Such cognitional endowment leads us out of the ethics of the 'timeless present' (Islam and Greenwood in Journal of Business Ethics 170: 1-4, 2021) towards ethical authenticity in business, leaving us better placed to reflect upon and address the ethical issues emanating from grand challenges like COVID-19.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.079 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 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".