Tending to the Emotional Experience of Cyber-Victimized Youth: How Teachers Can Support Victims of Severe Cyberbullying Incidents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cyberbullying negatively impacts the social-emotional development of youth and can interfere with school engagement and academic functioning. However, little is known about how teachers can support cyber-victims. This study aims to examine the specific support strategies that predict emotional relief from severe cyber-victimization. This study also identifies demographic and contextual determinants that further facilitate emotional relief when teacher support is provided. Using a within-subjects design, participants aged 12-to-17 years old rated the intensity of their emotions after being presented with vignettes about hypothetical cyberbullying scenarios. The study found that each type of teacher support predicted emotional relief in comparison to no support for each form of severe cyberbullying, but the amount of emotional relief varied across support types, demographic factors, and contextual factors. These findings have implications for early prevention methods for teachers of victimized youth.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it