"Off Orbit" or "Into the Wilderness": The Effect of Mentor-Student Relationship on Smartphone Dependence of Z-Generation Graduate Students
Bibliographic record
Abstract
Z-Generation (Gen Z) students have access to more information than any other generation at their age. Mentor-student relationship is the core interpersonal relationship during the graduate stage. How do Gen Z graduate students perceive the role of their mentor, when "anything they want to know is only a click away"? Based on Self-Determination Theory and Compensatory Internet Use Theory, this study investigated the relationship between mentor-student relationship and smartphone dependence among 1,432 Chinese social science and humanities (SSH) graduate students. A moderated mediation model was constructed to focus on the role of research self-efficacy and growth mindset. The results showed that a positive mentor-student relationship is found to have a significant negative predictive effect on smartphone dependence. Scientific self-efficacy is identified as a mediator in the relationship between the mentor-student relationship and smartphone dependence. Additionally, growth mindset is found to moderate the predictive effect of the mentor-student relationship on research self-efficacy. The results of this study revealed the mechanism of mentor-student relationship on graduate students' smartphone dependence, offering valuable insights for rethinking the role of mentors in the modern educational environment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".