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Record W4309713299 · doi:10.1177/14614448221136508

The social-ecological model of cyberbullying: Digital media as a predominant ecology in the everyday lives of youth

2022· article· en· W4309713299 on OpenAlexaff
Molly-Gloria Patel, Anabel Quan‐Haase

Bibliographic record

VenueNew Media & Society · 2022
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsWestern University
Fundersnot available
KeywordsSocial ecological modelEcological systems theoryEcologyEveryday lifeSocial mediaEcological psychologySociologySocial ecologyContext (archaeology)Media ecologyPsychologyComputer scienceSocial psychologyMedia studiesPolitical scienceGeographyBiologyWorld Wide Web

Abstract

fetched live from OpenAlex

While cyberbullying has been recognized as a critically important social problem, a void remains regarding the role of digital media. To address this gap, we propose the social-ecological model of cyberbullying, an expanded model that builds on Bronfenbrenner’s ecological systems theory (EST) and expands Swearer and Espelage’s social-ecological model of bullying. A strength of the proposed model is the addition of the digital context as a new ecology in the everyday lives of youth, which is closely interconnected with all the other systems. Furthermore, the model incorporates digital-specific factors within each ecological system of the original EST model. This provides scholars with a holistic model that they can test, finetune, and expand. A practical implication of the model is that it can guide the creation and implementation of effective and age-appropriate cyberbullying prevention and intervention approaches because it considers in the chronosystem life phases, life transitions, historical events, and crises.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.288
Teacher spread0.253 · 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 designQualitative
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

Citations31
Published2022
Admission routes1
Has abstractyes

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