MétaCan
Menu
Back to cohort
Record W94314740

The use of technology and electronic media in adolescent dating violence

2014· article· en· W94314740 on OpenAlexaffabout
Katherine Reif

Bibliographic record

VenueScholarship@Western (Western University) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicStalking, Cyberstalking, and Harassment
Canadian institutionsWestern University
Fundersnot available
KeywordsDating violencePsychologyPoison controlSuicide preventionInternet privacyComputer scienceMedical emergencyMedicineDomestic violence
DOInot available

Abstract

fetched live from OpenAlex

Abstract\nElectronic communication and social media have dramatically changed the way in which individuals communicate with one another. Through this shift, they have opened the doors for inappropriate and damaging behaviour to take place. Cyberbullying occurs when the internet is continuously used to insult or intimidate a person or persons in order to hurt them in a deliberate manner (Valkenburg et. al., 2010). In adolescent dating relationships, the online environment facilitates the way in which individuals who are or were dating continue to correspond. This closer proximity between individuals, however, enables abusive and controlling behaviours within these relationships to occur outside of face-to-face contact. This study examined adolescents’ perceptions of the severity of cyberbullying, motives, and the point in a dating relationship at which it is likely to become most severe. A mixed methodology was utilized within this study, using a sample of 70 grade 12 students at a high school in southwestern Ontario. It was found that cyberbullying behaviours are most likely to occur upon termination of a dating relationship, revenge is perceived as a common motive, and the severity of cyberbullying tends to be minimized.\nKeywords: Cyberbullying, bullying, gender, grade, age, violence, dating, adolescent, mixed-methods, focus groups

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.326
Teacher spread0.230 · 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 teacher head, 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

Citations1
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueScholarship@Western (Western University)Same topicStalking, Cyberstalking, and HarassmentFrench-language works237,207