Social Media, Not so Social: Exploring the Ethical and Administrative Implications of Cyberbullying Research as it Pertains to its Detection, Measurement, and Implementation of Preventative Strategies in Schools
Bibliographic record
Abstract
The digital revolution in the 21st century has paved the way for the proliferation of social-networking sites such as Facebook, Twitter, Instagram, Snapchat, TikTok, and others, which has helped to perpetuate civilization's age-old power imbalances in the form of cyberbullying. This paper examined how cyberbullying among adolescents are being detected, measured and mitigated, and what the ethical considerations are for school leaders. This conceptual research paper reviewed and analyzed 33 scholarly sources, belonging to a wide range of disciplines from cyber ethics to computer science. This analysis exposed cyberbullying as a social justice issue, plagued with gaps in trust, digital savviness, hierarchical structure, and policy initiatives. This paper invites school leaders to work within the Critical Transformative Leadership for Social Justice (CTLSJ) framework when navigating the ethical difficulties that may arise with cyberbullying detection, measurement and mitigation initiatives. Looking ahead, this paper urges the digitally novice adults to keep pace with the digitally native adolescents, and for policy makers to collaborate more with Influencers to help raise awareness around cyber ethics and digital citizenship among adolescents. Key words: Social-media, cyberbullying, critical transformative leadership for social justice, digitally novice, digitally native, Influencers, digital citizenship
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".