Estimating Technical Efficiency of Academic Departments of a Philippine Higher Education Institution
Bibliographic record
Abstract
The main thrust of this research is to measure the relative technical efficiency of the six (6) colleges of San Pedro College from school year 2004-2014. The technical efficiency of the academic units can be derived based on its ability to produce the optimum number of output (number of research outputs, number of graduates, and number of community extension conducted) based on a given set of inputs (budget allocation and ratio of the full-time and part-time faculty) using data envelopment analysis. The Nursing/Respiratory Therapy Department is consistent as the highest for the ratio of full-time to part-time faculty while the lowest ratio was observed by Medical Laboratory Sciences Department in 2016, Arts and Sciences in 2015 and Accounting and Business in 2014. In terms of technical efficiency, all departments are technically-efficient during 2014. The Nursing/Respiratory Therapy Department, Physical Therapy Department and Medical Laboratory Sciences Department did not obtain 100% efficiency. In 2016, only the Accounting and Business Management Department did not obtain full technical efficiency score. Further, using the Tobit model, the age of the department, number of baccalaureate teachers, proportion of faculty members with doctorate degree with those who are masters’ degree holders, and the dean’s qualification were found to be insignificant as sources of inefficiency.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".