The presentation of self in the digital age: Experiences of cyberbullying victims and perpetrators
Bibliographic record
Abstract
Virtual communication has become instrumental in the digital age and it presents advantages and risks, including cyberbullying, in the lives of young people. Drawing on Goffman’s (1959) concept of the presentation of self – the study of how the self assumes different roles and behaviours depending on social circumstances – I explore how young people with the lived experiences of cyberbullying engage in the presentation of their virtual and non-virtual selves and how they cope with the consequences of cyberbullying. Using a phenomenological framework for inquiry, the results of this study derive from qualitative interviews and participant-generated visual data. The results of this study suggest that there is no binary identity of a cyber-victim or cyber-perpetrator, and participants’ chosen identity shapes their presentation of self both in virtual and non-virtual settings as a way of coping and/or maintaining their status and appearance. Cyberbullying victimization is a form of online victimization, whereby the former form of victimization can produce digital harm and social inequalities due to the lack of emotional, affective, and mental health support offered to the victims of online bullying. With the recommendations for future research, this study advocates for creating spaces to offer mental health support to young people who experience cyberbullying victimization. Contributing to the growing field of digital criminology, the results of this thesis also suggest that the experience of cyberbullying normalizes the practice of online bullying among young people and shapes their understanding of online communication, victimization, and transgression in the digital age.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".