Cyberbullying Among Adolescents: Stakeholder-Driven Concept Mapping Approach
Bibliographic record
Abstract
BACKGROUND: Cyberbullying includes bullying behaviors on the Web; these behaviors are inconsistently measured and lack standardized definitions. The Uniform Definition of Bullying provides a consensus-based definition of bullying, and it highlights the need for an evidence-based definition of a model for cyberbullying. OBJECTIVE: Toward understanding the key elements and constructs defining cyberbullying, the objective of this study was to develop a stakeholder-driven conceptual model of cyberbullying. METHODS: Concept mapping is a validated research method that leverages both qualitative and quantitative approaches to integrate stakeholder input on complex topics. This process was used to develop a concept map and adapt it through participant input to a conceptual model. The validated concept mapping approach includes 5 steps: preparation, generation (brainstorming), structuring (sorting), representation (statistical analysis), and interpretation. We recruited stakeholder participants, including adolescents, as well as parents and professionals representing education, health, and the justice system. Analysis included hierarchical cluster analysis to develop a cluster map representing cyberbullying, followed by adaptation of that map to a conceptual model through qualitative participant feedback. RESULTS: A total of 177 participants contributed to the concept mapping process, including 69% females, 50% adults, and 68% Caucasian, representing each of our stakeholder groups. A total of 228 brainstorming items were generated and sorted into a concept map that included 9 clusters. Clusters included topics that had strong overlap with traditional bullying, such as consequences for perpetrators and targets, with example items "alienating" and "crippling." Some clusters were unique, such as cyberbullying techniques, with example item "excessive messaging," and characteristics of the cyberbullying experience, with example item "constant." Through the interpretation step, a conceptual model emerged, illustrating connections and distinctions between traditional bullying and cyberbullying. CONCLUSIONS: We found that in generating a stakeholder-driven concept map of cyberbullying, participants could not describe cyberbullying without integrating key concepts from traditional bullying. On the basis of our conceptual model, there are unique characteristics of cyberbullying that suggest that uniform definitions of bullying need to be evaluated to ensure their application to cyberbullying.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".