A Tracer Study of the Business Graduate Programs of a Catholic University in the Philippines
Bibliographic record
Abstract
The success of any degree program being delivered by higher education institutions is measured using the employment performance of its graduates as they navigate the dynamic labor market after completion of their studies. The tool used to gauge this metric is known as a graduate tracer study. Thus, this explanatory sequential mixed methods study may provide the graduate school the baseline employment information, the extent of practice of program competencies and demonstration of graduate attributes of business management major graduates spanning the years 2017-2021 and their level of satisfaction with the graduate programs. The results revealed that outcomes were achieved by the graduates as the graduate degree contributes to their development of competencies and these graduates were extremely satisfied with the delivery and implementation of the programs. They also demonstrated the attributes in their workplace. The results of the study are consistent with the emerging framework of the quality delivery of business graduate programs. It upholds that the satisfaction of the graduates in the delivery, and instruction of the programs, will result in the development of competencies, unlocking of knowledge, skills, and abilities and improve the social and economic status of the graduates.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".