Bringing Excitement to Empirical Business Ethics Research: Thoughts on the Future of Business Ethics
Bibliographic record
Abstract
(inspired by the title of the commentary by Babalola and van Gils). These editors, considering the diversity of empirical approaches in business ethics, envisage a future in which quantitative business ethics research is more bold and innovative, as well as reflexive about its techniques, and dialog between quantitative and qualitative research nourishes the enrichment of both. In their commentary, Babalola and van Gils argue that leadership research has stagnated with the use of too narrow a range of perspectives and methods and too many overlapping concepts. They propose that novel insights could be achieved by investigating the lived experience of leadership (through interviews, document analysis, archival data); by focusing on topics of concern to society; by employing different personal, philosophical, or cultural perspectives; and by turning the lens on the heroic leader (through "dark-side" and follower studies). Taking a provocative stance, Bal and Garcia-Lorenzo argue that we need radical voices in current times to enable a better understanding of the psychology underlying ethical transformations. Psychology can support business ethics by not shying away from grander ideas, going beyond the margins of "unethical behaviors harming the organization" and expanding the range of lenses used to studying behavior in context. In the arena of finance and business ethics, Guedhami, Liang, and Shailer emphasize novel data sets and innovative methods. Significantly, they stress that an understanding the intersection of finance and ethics is central to business ethics; financial equality and inclusion are persistent socio-economic and political concerns that are not always framed as ethics issues, yet relevant business policies and practices manifest ethical values. Finally, Charles Cho offers his opinion on the blurry line between the "ethical" versus "social" or "critical" aspects of accounting papers. The Journal of Business Ethics provides fertile ground for innovative, even radical, approaches to quantitative methods (see Zyphur and Pierides in J Bus Ethics 143(1):1-16, 10.1007/s10551-017-3549-8, 2017), as part of a broad goal of ethically reflecting on empirical research.
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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 | 0.053 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.062 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.040 | 0.050 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".