Development and validation of the trolling emotional action and response scale
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".