Sticks and Stones Can Break My Bones, But How Can Pixels Hurt Me?
Bibliographic record
Abstract
Educators and the public alike are often perplexed with the enormous and evolving cyber mise en scène. Youth of the digital generation are interacting in ways our fore-mothers and fathers never imagined — using electronic communications that until 30 years ago never existed. This article reports on a study of cyber-bullying conducted with students in grades 6 through 9 in five schools in British Columbia, Canada. Our intent was to quantify computer and cellular phone usage; to seek information on the type, extent and impact of cyber-bullying incidents from both bullies’ and victims’ perspectives; to delve into online behaviours such as harassment, labelling (gay, lesbian), negative language, sexual connotations; to solicit participants’ solutions to cyber-bullying; to canvass their opinions about cyber-bullying and to inquire into their reporting practices to school officials and other adults. This study provides insight into the growing problem of cyber-bullying and helps inform educators and policy-makers as to appropriate prevention or intervention measures to counter cyber-bullying.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.062 | 0.024 |
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".