Protective shielding, potential harm: a hermeneutic phenomenological study of the cyberbullying experiences of youth with visual impairment
Bibliographic record
Abstract
Cyberbullying was explored through the ‘lived experiences’ of youth with visual impairment (VI) and their schoolteachers in this study. A hermeneutic phenomenological design was used to reveal the rich details of how the 29 youth and five schoolteachers (n = 34) experienced cyberbullying in their different lifeworlds. Interviews and focus groups were conducted over a period of seven months. Three themes emerged: Sites of Cyberbullying, Coping Mechanisms and Sources of Help and Cybersecurity Awareness. Most youth with VI experienced traditional bullying in their school lifeworlds from school mates and teachers while in their cyber worlds, verbal bullying in the form of insults and body shaming from outsiders was common. Interestingly, most youth with VI participated in playing games on their mobile phones and in multiplayer games with sighted individuals. All 29 Malaysian youth participants demonstrated high anxiety towards cyberbullying and any potential threat, illuminating their lack of preparedness and training towards such threats. Protective policies by schools and parents designed to shield youth with VI may inadvertently yield the opposite results. Hence, this study further develops the theorisation of cyberbullying, and further research could include the perspectives of parents, police and school administrators.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".