Bibliographic record
Abstract
Incivility is a rapidly growing field of study in a number of academic domains. In workplace studies, it is clear that incivility is associated with a hostile and impoverished working environment. In post-secondary settings, it is equally associated with negative outcomes such as diminished class productivity, enjoyment, and participation. More recently, research has turned to studying incivility amongst younger adolescents. We continue this line of research by studying the link between the perpetration of incivility and adolescent health and wellbeing. Specifically, we examined whether participation in incivility would be correlated with indices of social, emotional, and mental health as measured by the Strengths and Difficulties Questionnaire (SDQ). As predicted, we found significant correlations with incivility and all five dimensions of the SDQ. A hierarchical linear regression revealed older age, less prosocial behavior, and emotional and conduct problems to be significant multivariate predictors of incivility perpetration. Implications for the health and wellbeing of school-aged adolescents are discussed. We further discuss that the results are largely consistent with a conceptualization of incivility as a lower-intensity antisocial behavior on the same continuum as higher intensity behaviors like bullying, characterized by goal-directed behavior that disregards and disrespects the rights and welfare of others.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".