Matching University Graduates’ Competences with Employers’ Needs in Taiwan
Bibliographic record
Abstract
The dramatic expansion of the number of higher educational institutions in Taiwan has contributed a great deal to the growing unemployment rate of university graduates. Given the accumulated number of students who graduated in previous years and failed to find a job, the pressure of finding a job is growing each year. On the other hand, however, many employers lamented that they are struggling to find qualified job candidates. The major reason for this mismatch is that the traditional university instruction that most graduates receive is no longer adequate for the changing demands of the new market, and employers are also failed to notice that the definition of a good job perceived by students is very different from decades ago. To address this mismatch, it is important to understand what employers want in graduates and what students are seeking in a job. By administering questionnaires to both employers and university students, we endeavor to identify the component of a good job perceived by students, and skills demanded by employers for work accomplishment. Questionnaires were administered to 250 students and 250 employers, and many differences between the two parties were identified. Suggestions were given for students, universities, and employers to narrow the talent gap between employers and university 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".