Reimagining the I3 Model: Addressing the Limitations of the I3 Model As Applied to Cyberbullying in Saskatchewan
Bibliographic record
Abstract
Purpose. The purpose of this research was to better understand issues of cyberbullying among Saskatchewan youth by applying a new metatheory of aggression, the I 3 Model. Theoretical Framework. The I 3 Model is a process-oriented metatheory of aggression which involves three core processes: instigating triggers (situational events that increase the likelihood of aggressive responses), impelling forces (influences that determine the strength of the aggressive response), and inhibiting forces (forces that decrease the likelihood of an aggressive response). Methods. Semi-structured interviews were employed with 16 educational professionals and community stakeholders. All data was digitally recorded, transcribed, and thematically analyzed. Results. Thematic analysis suggested impelling forces that included parental forces (lack of online monitoring, negative behaviour modelling, lack of digital literacy and control), institutional forces (overextended workloads of school personnel, lack of social availability, and diffusion of responsibility), ubiquitous accessibility to youth , and online disinhibition . Inhibiting forces identified included fostering empathy and digital citizenship education . Retaliatory cyberbullying was the only instigating trigger identified. Conclusion. Cyberbullying remains a significant problem in Canada, thus the need for a more complex theory to better account for all influences of online aggression is warranted.
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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 | 0.034 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".