St. Thomas University: Which Balanced Scorecard to Use?*/ST. THOMAS UNIVERSITY: LE CHOIX D'UN TABLEAU DE BORD
Bibliographic record
Abstract
ABSTRACT This case provides background on a university that undertook a formal strategic‐planning initiative. Mary White, Vice‐President (Administration), was responsible for developing the performance evaluation framework. She established a special task force, which included Peter Crimson, Assistant Vice‐President (Administration), Eva Black, Director of Financial Analysis, and Adam Green, Director of Planning and Analysis, to assist her in this endeavor. The special task force had an insightful discussion on the balanced scorecard during its first meeting. When the meeting concluded, Mary White asked Adam Green to conduct research on the different approaches to the balanced scorecard adopted in a number of universities. She expected Adam Green to provide an analysis of the different balanced scorecard approaches in universities, to make recommendations on the balanced scorecard approach that St. Thomas should adopt, and to develop a balanced scorecard for the university. An objective of the case is to provide students with an opportunity to evaluate the implementation of the balanced scorecard in universities, to recommend an approach for developing the performance evaluation framework, and to design a balanced scorecard for the university. RÉSUMÉ Le cas élaboré par l'auteure contient les données de base relatives au projet structuré de planification stratégique entrepris par une université. Mary White, vice‐présidente à l'administration, assumait la responsabilité de l'élaboration du cadre d'évaluation de la performance. Elle a constitué un groupe de travail spécial, réunissant Peter Crimson, adjoint à la vice‐présidente à l'administration, Eva Black, directrice de l'analyse financière, et Adam Green, directeur de la planification et de l'analyse, pour lui prêter main‐forte dans cette entreprise. Lors de sa première réunion, le groupe de travail spécial a tenu une discussion instructive sur le tableau de bord. Au terme de cette réunion, Mary White a demandé à Adam Green de faire des recherches sur la façon dont différentes universités ont choisi de traiter le tableau de bord. Elle souhaitait obtenir une analyse des différentes méthodes utilisées dans les universités, des recommandations quant au choix de la méthode appropriée à St. Thomas et l'élaboration du tableau de bord de l'université. Ce cas a notamment pour objectif d'offrir aux étudiants l'occasion d'évaluer la mise en application du tableau de bord dans les universités, de recommander une méthode d'élaboration du cadre d'évaluation de la performance et de concevoir un tableau de bord pour l'université.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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".