Understanding Bullying and Cyberbullying Through an Ecological Systems Framework: the Value of Qualitative Interviewing in a Mixed Methods Approach
Bibliographic record
Abstract
Recognized as complex and relational, researchers endorse a systems/social-ecological framework in examining bullying and cyberbullying. According to this framework, bullying and cyberbullying are examined across the nested social contexts in which youth live-encompassing individual features; relationships including family, peers, and educators; and ecological conditions such as digital technology. Qualitative inquiry of bullying and cyberbullying provides a research methodology capable of bringing to the fore salient discourses such as dominant social norms and otherwise invisible nuances such as motivations and dilemmas, which might not be accessed through quantitative studies. Through use of a longitudinal and multi-perspective mixed methods study, the purpose of the current paper is to demonstrate the ways qualitative interviews contextualize quantitative findings and to present novel discussion of how qualitative interviews explain and enrich the quantitative findings. The following thematic areas emerged and are discussed: augmenting quantitative findings through qualitative interviews, contextualizing new or rapidly evolving areas of research, capturing nuances and complexity of perspectives, and providing moments for self-reflection and opportunities for learning.
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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 | 0.061 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".