Investigation and Analysis of the Acceptance of the License Plate–Based Restriction Policy: A Case Study in Hangzhou, China
Bibliographic record
Abstract
In order to better understand the factors that affect Hangzhou residents’ acceptance of the license plate–based restriction (LPR) policy, new factors such as fairness, family life cycle factors, and preacceptance of alternative measures were added to explore new interactions between different factors. A questionnaire survey was completed among 958 residents of Hangzhou City, and a partial least squares structural equation model (PLS‐SEM) was established to analyze the factors that affect the acceptance of the LPR policy. An analysis of socioeconomic attributes is conducted to explore the impact of education, age, and family life cycle factors on the acceptance of the LPR policy. The results indicate that perceived cost‐effectiveness, social norms, policy cognition, fairness, important goals, and preacceptance of alternative measures have significant direct effects on the postacceptance of the LPR policy, while fairness and important goals have indirect effects through social norms. Regarding postacceptance, perceived effectiveness can only indirectly affect postacceptance of the LPR policy through policy cognition and perceived cost‐effectiveness. Responsibility attribution can only indirectly affect postacceptance through important goals. As the education level and age increase, residents’ acceptance of the LPR policy will decrease; young families without children and families with minor children have lower acceptance of the LPR policy than families with all adult members and elder families without children.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".