Opening the Governance of Business Schools: A Multi-Stakeholder View of Performance
Bibliographic record
Abstract
Business Schools, while ubiquitous in institutions of higher education, educating a significant proportion of graduates, face an unprecedented challenge to their legitimacy and suffer from the fragmentation of performance measures. Many measures are conferred by stakeholders disconnected from governments and policy makers responsible for funding higher education. This has a profound influence on both the management of business schools and the capacity of scholars to conduct rigorous, evidence-based research on performance in ways relevant to a wider spectrum of stakeholders. To confront this challenge, a multi-stakeholder working group (MSWG) was established to facilitate collaborative inter-institutional research focused at studying the management and practice of business schools. The MSWG defined a multi-phase, multi-year integrated research plan. The first phase, and the focus of this paper, identifies the most relevant outcome measures of business schools. The work began with extant assessments and identified of a new set of outcome measures, leading to the development of research instruments and associated data collection methods that will soon proceed to an empirical pilot test phase. The final instrumentation will represent a holistic and integrated business school scorecard that will become the foundation for all future research of the MSWG.
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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 | 0.031 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| 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".