Protocol for the design of an instrument to measure preadolescent children's self-report of covert aggression and bullying
Bibliographic record
Abstract
INTRODUCTION: Covert bullying in schools is associated with a range of academic, social, emotional and physical health problems. Much research has focused on bullying, but there remains a gap in understanding about covert aggression and how to most accurately and reliably measure children's own reports of this behaviour. This paper reviews relevant literature and outlines a research project that aims to develop a self-report instrument that effectively measures covert aggression and bullying. It is anticipated that this research will result in a standardised instrument that is suitable for exploring preadolescent children's experiences of covert aggressive behaviour. The data collected by the instrument will enhance health and education professionals understanding of covert bullying behaviours and will inform the design and evaluation of interventions. METHODS AND ANALYSIS: Relational developmental systems theory will guide the design of an online self-report instrument. The first phase of the project will include a critical review of the research literature, focus groups with children aged 8-12 years (grades 4-6) in Perth, Western Australia, and expert review. The instrument will be explored for content and face validity prior to the assessment of convergent and discriminant validity, internal consistency and test-retest reliability. ETHICS AND DISSEMINATION: The study has been approved by the Curtin University of Human Research Ethics Committee (RDHS-38-15) and by the Executive Principal of the participating school.
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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 | 0.060 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.138 | 0.036 |
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".