Ethics and the Academy: Lessons from Business Ethics and the Private Sector
Bibliographic record
Abstract
Most academics recognize that universities, as institutions, have an obligation to account rigorously for financial expenditures. There is less agreement about teaching and research wherein issues of autonomy and academic freedom enter the debate. Yet here too, demands for accountability are being pressed on the academy. In recent years, the demand for accountability also has been directed with considerable force to the private sector with what appear, in a number of cases, to be dramatic effects. Equally dramatic has been the extent to which the public debate and the response of the private sector to public criticism have linked issues of accountability to ethics. Of particular interest is the idea that accountability is not just a managerial, organizational or political concept. It is also a moral concept, a concept, furthermore, that is central to understanding the status and legitimacy of the modern corporation. My purpose in this paper is to explore this insight and to develop its relevance for understanding and responding to the crisis which contemporary university systems are currently experiencing. I do so not with the idea of persuading the reader that universities should be understood to be, or managed as though they were, private sector corporations. To the contrary, close study of the sort I propose should help to identify important similarities, but also key differences. Both are central to understand- ing what accountability requires for the contemporary university.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.065 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".