Sticks and Stones Can Break My Bones, But How Can Pixels Hurt Me?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it