Cyber-bullying: Policy response and perceive impacts in a Saskatchewan school division
Bibliographic record
Abstract
The purpose of my research was to analyze the process utilized by a Saskatchewan school division to create policy that addressed the issue of cyber-bullying. The research and data collection for this case-study was performed during the time frame of January 2008 to June of 2009 and occurred after the Saskatchewan school division had completed a policy that addressed the issue of cyber-bullying. The research focused on the school division’s use of the policy process with no intent of evaluating the policy itself. This policy study was a qualitative case study designed to develop an understanding of the policy response of a Saskatchewan school division in addressing the legal and social issues related to cyber-bullying. The analytical framework for this case-study was drawn from a staged policy analysis model reflecting complementary aspects of rational and critical policy analysis models (Ball, 1998; Blaikie, 2000; Dye, 2002; Levin, 2001). Semi-structured interviews were performed with school division personnel who were responsible for the design and implementation of an Anti-Bullying policy for their school division. All data collected was coded into one of four broad categories: policy origin, policy adoption, policy implementation, or perceived policy impacts. Through a descriptive investigation I provided an account of the manner in which policy issues related to cyber-bullying were posed, of the explanations constructed, and of the policy directions formulated by a Saskatchewan school division. The analysis of the interview data revealed the influence of powerful stakeholders on the policy process, the existence of blended layers throughout the dynamic policy cycle, and the significance of each stage of the policy cycle in creating an effective policy in an ever-changing society.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".