Undergraduates’ Political Participation Behaviors in Public Universities of Hebei Province, China
Bibliographic record
Abstract
Political participation is a necessity of human life and the level of it reflects the degree of democracy which can be considered not only the right but also the obligation. Hebei province as a populous province has a large population of undergraduates, especially the expansion the university education policy carrying out in 2008. For the undergraduates in Hebei Province, they do not have adequate political knowledge, political skills and rarely practice in political activities. The objective of this article is to propose and evaluate students’ political participation behaviors in Hebei public universities of China. Data of this article is based on two sources; primary data were collected through questionnaire and 1990 informants were selected based on the cluster sampling method, the main statistical method for evaluation of research hypotheses is on the basis of on the basis of SmartPLS and SPSS software, meanwhile, secondary data which were collected from journal articles, reports and so on. Findings of this study indicate that, the level of students’ political participation was low in public universities in Hebei province in China. Moreover, the author elaborated four reasons that led to the low political participation behaviors in public universities of Hebei Province, which were the weak economic foundation, the backwardness of the cultural environment, the unsound political system and the influence of traditional culture. In addition, the author suggested that political participation among Hebei province public universities students need to improve and develop.
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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 | 0.001 | 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".