Online Peers and Delinquency: Distinguishing Influence, Selection, and Receptivity Effects for Offline and Online Peers with Longitudinal Data
Bibliographic record
Abstract
Abstract The field of criminology has spent nearly a century investigating the link between peers and delinquency, but only recently turned its attention to the online peer context. We examine three ways online and offline peer delinquency are related to self-reported delinquency. In theory, online peer delinquency may influence delinquent behavior independently of the influence from the physical presence of delinquent peers. Adolescents may also select online peers who are similar to their offline peers, and experiences online may contribute to being more receptive to offline peer influence. We use survey data from a longitudinal sample of middle and high school students in a large, metropolitan area, which includes measures of online peer support for delinquency and perceived delinquency of offline peers. Employing path models, we find that perceiving to have offline delinquent peers is partly related to previous behavior but also to previous experiences with online friends. We also find that the measures of both offline and online peer delinquency are independently related to later self-reported delinquency, and online peer support for violence can enhance the apparent influence of offline violent peers. Overall, this study illustrates that research examining delinquent peer influence should also include online peer processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".