A Review of School Board Cyberbullying Policies in Alberta
Bibliographic record
Abstract
An online search for school board cyberbullying/bullying policies in Alberta was conducted. The results showed that while only five school boards had a bullying policy, many schools had technology or Internet use guidelines. The online search included an assessment of one extensive school board cyberbullying policy as well as Internet use guidelines in two large school boards in Alberta. While technology and Internet use guidelines support anti-bullying initiatives, it is argued that a clear well defined policy empowers administrators to make informed decisions on how to handle cyberbullying. Finally, policy recommendations are proposed based on the results of the online search. On a entrepris une recherche en ligne pour trouver les politiques des conseils scolaires albertains en matière d’intimidation/harcèlement en ligne. Les résultats indiquent que si seulement cinq conseils ont mis en place une politique en matière d’intimidation, plusieurs écoles ont des directives quant à l’emploi des technologies et de l’internet. La recherche en ligne a comporté l’évaluation d’une politique scolaire approfondie sur le harcèlement en ligne ainsi que les directives quant à l’emploi de l’internet de deux grands conseils scolaires en Alberta. Bien que les directives en matière de l’emploi des technologies et de l’internet appuient les initiatives contre le harcèlement, nous affirmons que la mise en place d’une politique clairement définie permet aux administrateurs de prendre des décisions éclairées quant à la gestion du harcèlement en ligne. Nous terminons en proposant des recommandations stratégiques reposant sur les résultats de la recherche en ligne.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.032 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".