Cyberbullying on social networking sites: A literature review
Bibliographic record
Abstract
Cyberbullying or electronic hostility has as of now been assigned as a genuine general wellbeing danger. Cyberbullying ought to likewise be considered as a reason for new beginning mental side effects, physical manifestations of hazy etiology, or a drop in scholarly execution. Pediatricians ought to be prepared to assume a significant part in focusing on and supporting the social and formative prosperity of youngsters. Cyberbullying or electronic hostility has as of now been assigned as a genuine general wellbeing danger and evoked alerts to the overall population from the Centers for Disease Control and Prevention (CDC) [1]. The term seems to have been begotten in 2000 in Canada by the proprietor of a Web website committed to forestalling customary (up close and personal) tormenting [1]. Tokunaga characterized the wonder as any conduct performed through electronic or computerized media by people or gatherings that over and again imparts antagonistic or forceful messages planned to perpetrate damage or distress on others [2]. This definition features a few significant cyberbullying highlights: the innovation part, the unfriendly idea of the demonstration, the plan to cause enduring, considered by most researchers to be critical to the definition, and monotony
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".