Mapping Stakeholder Expectations of a Publicly Funded Post-Secondary Institution: A Balanced Scorecard Perspective
Bibliographic record
Abstract
ABSTRACT Reservations are generally expressed about the applicability of performance management tools to public sector institutions. One of the concerns relates to the corporatization of public sector entities, which can lead to the erosion of the public interest. Sensitivities are especially high with higher education. However, increasing budget deficits and a focus on financial stewardship is driving more public entities toward the adoption of these techniques. One tool that has become widely adopted in this regard is the balanced scorecard (BSC). We attempt to demonstrate the applicability of the BSC model to publicly funded post-secondary institutions by developing a strategy map based upon stakeholder expectations. Multi-dimensional relationships between key success factors (KSFs) are explored with the decision-making trial and evaluation laboratory method. Our paper contributes to the public policy debate on the purported role of higher education while identifying causal relationships between KSFs for the purpose of strategy implementation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".