Isolation, cohesion and contingent network effects: the case of school attachment and engagement
Bibliographic record
Abstract
Abstract Isolation and cohesion are two key network features, often used to predict outcomes like mental health and deviance. More cohesive settings tend to have better outcomes, while isolates tend to fare worse than their more integrated peers. A common assumption of past work is that the effect of cohesion is universal, so that all actors get the same benefits of being in a socially cohesive environment. Here, we suggest that the effect of cohesion is universal only for specific types of outcomes. For other outcomes, experiencing the benefits of cohesion depends on an individual’s position in the network, such as whether or not an individual has any social ties. Network processes thus operate at both the individual and contextual level, and we employ hierarchical linear models to analyze these jointly to arrive at a full picture of how networks matter. We explore these ideas using the case of adolescents in schools (using Add Health data), focusing on the effect of isolation and cohesion on two outcomes, school attachment and academic engagement. We find that cohesion has a uniform effect in the case of engagement but not attachment. Only non-isolates experience stronger feelings of attachment as cohesion increases, while all students, both isolates and non-isolates, are more strongly engaged in high cohesion settings. Overall, the results show the importance of taking a systematic, multi-level approach, with important implications for studies of health and deviance.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".