College Students’ Choice Behavior of Electric Two-Wheeled Vehicle
Bibliographic record
Abstract
Many countries have made great efforts to boost the use of electric vehicles in recent years; for example, advanced countries including Norway and the Netherlands in Europe and the United States have enhanced people’s willingness to use electric vehicles by means of appropriate subsidies and suppression of private vehicles. In Asia, Taiwan has been promoting the policy of replacing traditional fuel two-wheeled vehicles (FTWVs) with electric two-wheeled vehicles (ETWVs) and strengthening the policy by means of replacing a large number of old FTWVs and subsidizing the purchase of ETWVs. This study took college students as the subjects, as they were the first potential group to buy ETWVs, and their concept of environmental sustainability can be shaped for cultivating vehicle use habits. This study applies a questionnaire to probe into the ETWV usage preferences of college students and explores the significant factors affecting college students’ purchase of ETWVs. This study uses a mixed logit (MXL) model for estimation. The results of model estimation show that those who are younger, have higher income, have good experience in using ETWVs, and are in user-friendly external traffic environments, are more inclined to choose ETWVs. In the future, government units can formulate policies to promote ETWVs according to the characteristics of different relevant factors.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".