Developmental cascade models linking peer victimization, depression, and academic achievement in Chinese children
Bibliographic record
Abstract
= 10.16 years, SD = 2 months) attending elementary schools in Shanghai, People's Republic of China. Three waves of data on peer victimization, depression, and academic achievement were collected from peer nominations, self-reports, and school records, respectively. The results indicated that peer victimization had both direct and indirect effects on later depression and academic achievement. Depression also had both direct and indirect negative effects on later academic achievement, but demonstrated only an indirect effect on later peer victimization. Finally, academic achievement had both direct and indirect negative effects on later peer victimization and depression. The findings show that there are cross-cultural similarities and differences in the various transactions that exist among peer victimization, depression, and academic achievement. Statement of contribution What is already known on this subject? Peer victimization directly and indirectly relates to depression and academic achievement. Depression directly and indirectly relates to academic achievement. Academic achievement directly and indirectly relates to depression. What the present study adds? A developmental cascade approach was used to assess the interrelations among peer victimization, depression, and academic achievement. Academic achievement mediates the relation between peer victimization and depression. Depression is related to peer victimization through academic achievement. Academic achievement directly and indirectly relates to peer victimization. Academic achievement is related to depression through peer victimization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".