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Record W2163519263 · doi:10.2190/8yqm-b04h-pg4d-bllh

Cyber-Harassment: A Study of a New Method for an Old Behavior

2005· article· en· W2163519263 on OpenAlexaffabout
Tanya Beran Qing Li

Bibliographic record

VenueJournal of Educational Computing Research · 2005
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHarassmentAngerCyber bullyingSadnessPsychologyQuarter (Canadian coin)Variety (cybernetics)Social psychologyThe InternetComputer science

Abstract

fetched live from OpenAlex

A total of 432 students from grades 7–9 in Canadian schools reported their experiences of cyber-harassment, which is a form of harassment that occurs through the use of electronic communications such as e-mail and cell phones. More than two-thirds of students (69%) have heard of incidents of cyber-harassment, about one quarter (21%) have been harassed several times, and a few students (3%) admitted engaging in this form of harassment. In addition, victims of cyber-harassment reported a variety of negative consequences, especially anger and sadness, and had experienced other forms of harassment. These results suggest several avenues of research needed to explain how and why adolescents use technological advances to harass their peers.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score0.446

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0110.004
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.180
GPT teacher head0.556
Teacher spread0.377 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations668
Published2005
Admission routes2
Has abstractyes

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