Is Drama the New Bullying? Differentiating Bullying and Drama Among College Students
Bibliographic record
Abstract
Compared to research on bullying in schools and in the workplace, little is known about the prevalence and typology of bullying among college age-students.Recent research suggests that young adults may be less in favour of the term "bullying", but more open to the notion of "drama".The concept of drama has existed for some time, and is common vernacular in both media and entertainment, as well as among students and teachers.Though drama has been conceptually linked to bullying, researchers have only just begun to explore this behaviour.The goal of this dissertation was to better understand this construct of drama, and explore it in the context of bullying amongst college students.A total of five studies were conducted using first-and second-year college students exploring conceptualizations of bullying and drama, associations with common forms of aggression, personality profiles of those who engage in bullying and drama, socialcognitive predictors of behavioural decision-making, and the consequences of these behaviours on mental health and well-being.Results indicated that while there is conceptual overlap between bullying and drama, the behaviours are measurably distinct, particularly in terms of the forms of aggression they are associated with, and the traits of those who engage in these behaviours.That being said, the decision-making precursors to engagement in drama appear similar to those involved in bullying: attitudes and norms predict intent and willingness to engage in both behaviours.Finally, bullying seems to remain a salient behaviour among college students, with continued concerns for mental health and well-being, and more importantly, drama appears also to have harmful effects on the psychological adjustment of those who engage in it.The results are discussed in been possible.Thank you for your patience and for pushing me to succeed.Many thanks also to Dr. Anne Bowker, who graciously supported me over the last year.I would also like to extend my appreciation to the members of my committee, Dr. Andrea Howard and Dr. Cheryl Harasymchuk for offering excellent wisdom and expertise on how to make this study the best it could be.Finally, I would like to express my gratitude to my amazing husband, and my wonderful family and friends for their constant positivity, endless motivation, and unwavering love and support over the last few years
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".