The Moral Responsibilities of Business Schools –In Pursuit of Excellence and Social Justice
Bibliographic record
Abstract
In recent years, many business schools have struggled -- particularly as Generation Z (Gen Z) and Millennial students have questioned the practical value of a four-year business degree (Salhotra, 2022) and have expressed dissatisfaction with a work world that increasingly treats employees like commodities (Maloni, Hiatt & Campbell, 2019). Coupled with the pressures of inflation (June, 2022) and the paucity of funds available from both governmental sources and private donations (Zusman, 2005), the typical secondary school has attempted to make do financially by increasingly relying on part-time adjunct and untenured faculty to whom they offer fewer benefits and lower salaries (Colby, 2023). This approach to cutting costs has predictably lowered the quality of education (Bettinger & Long, 2010) despite the fact that business schools have long been criticized about the relevance of their courses and the low quality of business graduates (Mintzberg, 2004).The purpose of this special edition is to address selected social justice issues facing society that also affect business schools. These papers have substantial importance for society in the 21st century. Each of these papers addresses factors associated with the moral responsibilities of business education in a world where business schools are under tremendous pressure to improve their quality – despite the fact that their resources are often limited. We begin the paper by briefly addressing the general issue of moral responsibility and its specific application to schools of business. We define moral responsibility, identify duties owed to several business school stakeholders, and offer six recommendations for business school administrators, accrediting bodies, and faculty. We then briefly introduce the articles contained in this special edition and explain their contribution to social justice issues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".