Bibliographic record
Abstract
[Abstract] Using survey data on course selection after graduation whose subjects were senior high school students attending mock lesson at Kyushu Junior college of Kinki University for 5 years from 18^ through 24^ of Hesei period, we examined an association between their response and an actual course selection. There was a significant difference between female and male students with respect to the proportion of students who advanced to university or junior college（p＜0.001）. The proportion of students whose course selection after their graduation was a technical college（27.5％）was higher than national average（i.e., 17.0％）by 10.5％. Furthermore, of high school students who selected “others” as their course selection after graduation, many of them could not decide their courses after their graduation. Since half of them（50.0％） went to a technical college /polytechnic, and more than quarter of them（27.3 ％）went a university, its necessity to have intensive aggressive counseling for the choice of their future course.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".