A tribute to the late Dr. W. Michael Hoffman: Putting business ethics theory into practice
Bibliographic record
Abstract
Abstract This article is a tribute to the late Dr. W. Michael Hoffman's life and professional career (1943–2018), including his important contribution to the business ethics academic community, as well as to the practical world of business. Following a brief summary of Dr. Hoffman's professional achievements, several tributes are provided including from Professor Richard De George, columnist Gael O'Brien, and Professor Patricia Werhane. The tributes are followed by synopses of a small sample of Dr. Hoffman's many journal articles published in several different business ethics journals spanning 35 years (1982–2017). The tribute concludes with a personal note of gratitude by the author to Dr. Hoffman.
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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 | 0.022 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".