Internet impropriety: Moral identity, moral disengagement, and antisocial online behavior within an early adolescent to young adult sample
Bibliographic record
Abstract
INTRODUCTION: Information and Communication Technologies (ICT) are the most popular medium for social communication amongst adolescents and young adults. However, there is growing concern surrounding heightened ICT use and the activation of influential social constructs such as moral identity and moral disengagement. The importance of moral ideals to oneself (i.e., moral identity) and the distancing of oneself from these moral ideals (i.e., moral disengagement) are often contextual and were tested for differences in online domains compared to face-to-face interactions. METHODS: = 19.54 years, SD = 4.48) completed self-report questionnaires that assessed online and face-to-face behavior in this cross-sectional study. RESULTS: Moral identity in an online context was significantly lower when compared to family and friend contexts. Further, moral disengagement was significantly higher in an online context when compared to face-to-face contexts and online moral disengagement significantly mediated the relationship between online moral identity and antisocial online behaviors (i.e., pirating, trolling, and hacking, etc.,). Both of these contextual differences remained stable across early adolescence to young adulthood. CONCLUSION: Moral identity and moral disengagement exhibit sociocognitive effects within online contexts across ages of early developmental importance. These results may account for high prevalence rates of antisocial online behavior such as trolling, pirating, and hacking within this sample. As social interaction for younger demographics continues to gravitate online, these results highlight that online contexts can influence important personality constructs.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".