<i>Business Ethics Quarterly</i> Special Issue on: Normativity in Business Ethics and Beyond
Bibliographic record
Abstract
The purpose of this special issue is to address fundamental questions about a possible deficit of normativity in management research, practice, and education-especially as it relates to the field of business ethics and its neighboring disciplines, corporate social responsibility (CSR) and sustainability.Questions regarding the role of normativity are familiar in many respects and have been the subject of long-standing debates in BEQ (e.g., with respect to the "separation thesis").These questions are of renewed importance, if not urgency, against the backdrop of 1) the so-called grand challenges (George et al. 2016) and 2) the meteoric rise of dominantly descriptively and functionally oriented CSR and sustainability research and teaching.By "normativity" in this context, we refer to explicit and implicit values-driven decision-making that is action guiding (i.e., prescriptive).The domain of the normative covers all forms of business decisionmaking, such as addressing systemic injustice, grand societal challenges, organizational narratives, institutional arrangements, corporate purposes, economic optimization norms, broader systemic design, and the decisions of individual managers.We seek different perspectives on the possible deficit of normativity in business, including alternative views suggesting that the problem is overstated or concern is misplaced. Aims and ScopeWe aim to take stock of the perceived decline in normativity in the field of business ethics (within which we include "micro" or individual-level behavioral ethics research and the neighboring fields of CSR and sustainability) and consider its implications for the field and beyond.Concern about the role of normativity in business ethics ranges over questions about the adequacy or usefulness of research that disregards normative considerations or implications, empirical research that does not come clean about normative presuppositions, empirical methods that squeeze out normative methods, and instrumental "business cases" that dominate ethics teaching.Fundamentally, these concerns reflect uncertainty, if not disagreements, about the identity of the field writ large.Although we applaud the growing interest in business ethics-both as a field in itself and as the subject of research in other fields-we are concerned about recent developments, especially the apparent entrenchment of two camps in the field of business ethics.One is empirically oriented, investigating issues of CSR and sustainability of various kinds, often with largely managerial and instrumental intentions.The other is focused on normative issues, asking "what is the right thing to do?" and aiming to provide normative reflections and orientations for business.To be sure, there are echoes here of debates within the field that fuel or reject this bifurcation dating back decades (e.g.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.106 | 0.032 |
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".