Influential Factors on Political Participation of Public University Undergraduates in Hebei Province, China
Bibliographic record
Abstract
In recent decades, political participation amongst young people has attracted much academic research in established democracies. However, as an understudied area in China especially in Hebei province, political participation of public university undergraduates is in low level. Thus, this article concerns the political participation of public university undergraduates in Hebei province of China. The objective of this article is to identify the influential factors and determine the principal influential factor to students’ political participation in the public university of Hebei province. Therefore, 1990 respondents were selected based on the cluster sampling method, the main statistical method for evaluation of research hypotheses is by the PLS-SEM. Findings of this study indicate that political education has neither effect nor relation to political participation, whereas university identity, experience, major integrated undertake directly function on political participation. Moreover, political competence is the most important for students’ political participation in the public universities of Hebei province, China. Political value cognitive ability is the most important competence which influences the level of political participation among all involved factors in influencing students’ political participation in Hebei public universities according to the results of Importance-Performance Map Analysis (IPMA) and Structural Equation Modeling (SEM). Additionally, the author suggested that in order to improve students’ participatory level, enhancing capability of students is a necessary way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".