The Social Responsibility of Managers: Reassessing and Integrating Diverse Perspectives
Bibliographic record
Abstract
The social responsibility of business has been a prominent issue in the academic and practitioner literatures, as well as in the curricula of business schools, for many years. While Friedman's iconic defense of profit maximization as the responsibility of management has been widely and extensively assailed, emerging positions on the role of business in society offer little clear and practical guidance to current managers, as well as Masters of Business Administration students. I argue in this article that the focus of the debate should shift to considering how the rules of the game surrounding business' behavior should be formulated and what the role of socially responsible managers should be in helping to establish those rules. My contention is that the goal of society should be to strengthen the linkage between the achievement of social objectives and profit maximization by business through “bright line” regulations and laws. Socially responsible managers will participate in setting the rules of the game by advising policymakers on how specific regulations and laws can be structured so that they most effectively condition the linkage between social objectives and profit maximization. If there is a unique social responsibility of managers beyond profit maximization, it is to participate in the policymaking process “honestly,” that is, without attempting to game the system through guile and opportunism.
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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 | 0.050 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".