University Efficiency: A Comparison of Results from Stochastic and Non-Stochastic Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Efficiency scores are determined for Canadian universities using both Data Envelopment Analysis (DEA) and stochastic frontier (SF) methods for selected specifications. The scores are compared. Although there is some consistency, there is also considerable divergence in the efficiency scores and their rankings. Besides choice of the DEA or SF method, scores are sensitive to the definition of output, the inclusion of environmental (i.e., primarily firm-specific) factors, and the procedures for their inclusion (one-stage and two-stage methods). Despite the divergence among methods and specifications noted, the relative positions of individual universities across sets of several efficiency rankings (e.g., all the DEA and SF outcomes) demonstrate consistency. An analysis of rankings provides a range of potential rankings for each university. High and low efficiency groups are evidenced but the rank for most universities is not significantly different from that of many others. The results indicate that, while efficiency analysis can be helpful, decision makers need to be very cautious when employing efficiency scores for management and policy purposes and they recommend looking for confirmation of the usual efficiency analysis.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 it