Professional Ethics of Students in Secondary Vocational School
Bibliographic record
Abstract
In the education system, Chinese students are required to acquire professional ethics education criteria. However, it was found that graduates mainly fail in professional ethics education. This study aimed first to examine the current state of vocational ethics, second to investigate the problems in vocational ethics among students, and third to provide guidelines for enhancing vocational ethics among students at the secondary vocational school of Dazhou Vocational Senior High School. The samples are 186 teachers and 382 students. Questionnaires and interviews are used to collect data. Frequency, percentage and content analysis are conducted. The findings are as follows: First, family members lack a solid understanding of educational concepts, and second, the school’s moral education institutions are not entirely effective. The teacher’s professional moral education methods are insufficient for innovation; the timeliness is not stable and incompetent; third, in a working enterprise environment, students fail to meet its requirements. In addition, irrational teaching and learning are disconnected between the students and teachers. Students often neglect vocational morals, and the cultivation of vocational morals is low. Moreover, the guidelines for enhancing vocational ethics were declined due to the rapid development in China, but students’ public moral consciousness is slow to develop. Those factors affect the students’ correct views.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".