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Record W4233830984 · doi:10.1504/ijwbc.2021.116637

Development and validation of the trolling emotional action and response scale

2021· article· en· W4233830984 on OpenAlexaboutno aff
Abigail S. Ginader, Pooja Rana, Marney A. White

Bibliographic record

VenueInternational Journal of Web Based Communities · 2021
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyPsychologyScale (ratio)Action (physics)Coping (psychology)Applied psychologyThe InternetReliability (semiconductor)Emotional intelligenceValiditySocial psychologyClinical psychologyPsychometricsComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The aim of this scale was to understand the emotional effects of trolling on internet users, as well as coping strategies. Pilot testing was conducted with 26 students and one expert who is a professor of public health and psychiatry. The results of the pilot were used to develop the 22-item scale. Data were collected from 347 participants via social media platforms and analysed using SPSS. The Toronto empathy scale was used as a validity index. Three sub-scales were developed: emotional experience of trolled targets, emotional experience of bystanders, and action of bystanders. Reliability among the five items of the emotional experience of trolled targets sub-scale was 0.779, among the three items of the emotional experience of bystanders sub-scale was 0.678, and among the two item of the action of bystanders sub-scale was 0.594. The Toronto empathy scale was significantly correlated with each of the three sub-scales.

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)0.015
metaresearch head score (Gemma)0.031
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: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.031
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.046
GPT teacher head0.319
Teacher spread0.273 · 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
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Web Based CommunitiesSame topicBullying, Victimization, and AggressionFrench-language works237,207