Social contagion and high school dropout: The role of friends, romantic partners, and siblings.
Bibliographic record
Abstract
Social contagion theories suggest that adolescents in relationships with same-age high school dropouts should be at a greater risk of dropping out themselves. Yet, few studies have examined this premise, and none have considered all potentially influential same-age intimates, focusing instead on only either friends or siblings. Moreover, a key influence in adolescents’ social worlds, romantic partners, has been ignored. The goal of this study was to provide a comprehensive view of dropout contagion by considering occurrences of dropout among friends, siblings, and romantic partners. Data came from a sample of Canadian adolescents (N = 545) comprising one third of high school dropouts; a second third of carefully matched at-risk but persevering schoolmates; and a last third of average, not-at-risk students. As predicted, adolescents were at greater risk of dropping out when a member of their network had recently left school (i.e., in the past year, OR = 3.11; 95% CI [1.78, 6.27]), with independent associations of nontrivial sizes for occurrences of dropout among friends, romantic partners, and siblings (ORs between 1.97 [95% CI 1.25, 3.41] and 3.12 [95% CI 1.23, 11.0]). Moreover, adolescents seemed particularly at risk of quitting school (OR = 4.88; 95% CI [2.54, 12.5]) when their networks included more than one type of same-age intimate (e.g., a friend and a sibling) who had recently dropped out. Findings suggest that social contagion of dropout is a pervasive phenomenon in low-income schools and that prevention programs should target adolescents with same-age intimates who have recently left school.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".