Exploration of the Motivation of Secondary Vocational Education Under the Background of “Diversion”—Based on a Case Study of Impoverished Junior High School Students in Northwest Rural Areas of China
Bibliographic record
Abstract
The society generally regards the students who choose the secondary vocational school as the students who failed the high school entrance examination. Based on interviews and on-site observations of poor junior high school students and their parents in a rural junior high school in Northwest China, the paper summarizes the four major motivations for rural poor students to enter secondary vocational schools under the background of today’ s high school entrance examination diversion. The cost of educational choices and compromises, the influence of peer groups, and individual interests are motivated by school, family, peer, and individual motives, respectively. In view of the current fierce competition in education in China, the disparity of educational resources between regions and the embarrassed family background, the decision to enter secondary vocational school is the result of the interaction between the macro-structure and micro-situation of poor rural junior high school students. In conclusion, promotion to secondary vocational education is a reasonable choice for many rural families. Therefore, in order to meet the survival and development needs of the disadvantaged rural poor students and their families, and make vocational education truly a channel for their upward mobility, it is necessary to strengthen the publicity of vocational education, change the concept of discrimination against vocational education, and at the same time strive to improve vocational education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".