The Influence of Social Connectedness Stimulation on Prosocial Behavior Based on Hierarchical Linear Model-Taking Universities in Guangxi, China as an Example
Bibliographic record
Abstract
The results of the study indicate that the more social support a student gets, the higher the interpersonal trust, the more harmonious interpersonal relationship, and the higher the willingness to conduct prosocial behavior. The objective of this study is to examine the sense of social connectedness (SSC) and prosocial behavior (PB) of students in Guangxi higher education institutions in China and to further understand the factors influencing PB of students in higher education. In this study, a total of 1,007 students were sampled from eight Guangxi higher vocational schools through purposive sampling using questionnaires, of which 676 (67.1%) were male students and 331 (32.9%) were female students. This study further enriches self-determination theory by exploring the effects of teachers’ character, teaching behaviors, and social support on PB, using SSC as an intrinsic motivation. In addition, the study results revealed that SSC, perceived teacher character, teaching behaviors, and perceived social support (PSS) were positively related to students’ PB. SSC affects PB not only directly but also indirectly through the mediating role of perceived teacher character teaching behavior and perceived teacher support. Teacher character, teaching behaviors, and PSS also play a chain mediating role in the relationship between SSC and PB. Finally, this study provides strategies to optimize school character management for higher education students to meet their basic psychological needs and thus promote the production of PB.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".