The Efficacy of Non-Anonymous Measures of Bullying
Bibliographic record
Abstract
The Olweus checklist, along with most of the questionnaires commonly used in bullying research, is anonymous. The respondent is not required to put down his/her name. This has been accepted as the ‘best suited’ method of assessing bullying. However, this assumption has not been adequately tested, and there is contrary evidence that this method is more conducive to obtaining more truthful responses from the respondents. This study tested the issue of anonymity versus non-anonymity experimentally using a balanced design. A total of 562 elementary school children (grades 1-8) from two inner-city schools in Toronto took part in the study. The findings supported the hypotheses that the respondents did not differ in their report of the incidence of either bullying or victimization, regardless of whether they were required to identify themselves by writing down their names on the questionnaire forms. The advantages of using non-anonymous questionnaires in bullying and victimization research, as well as in intervention work in schools, are highlighted.
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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 | 0.031 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".