Bibliographic record
Abstract
Results from two studies conducted at a mixed-ethnic elementary school in Canada are presented to demonstrate the potential for bystanders to stop verbal bullying. Name-calling is one of the most common forms of bullying, and leaving it unchecked fosters a tolerance for intergroup discrimination. Because it occurs in unsupervised places, peer bystanders must play a role in stopping it. The results of our survey with 204 students from third to sixth grade indicated that 60% witnessed bullying in the previous four weeks: 28% verbal, 24% social, and 23% physical. Compared to third graders, sixth graders witnessed more bullying and felt more bothered about it, yet fewer tried to intervene (10% compared to 22% for third graders). The second study used a modelling and role-playing paradigm to study the kinds of verbal intervention students felt comfortable making. Students from younger and older grade levels heard an audio-taped name-calling scenario with an ingroup bully and an outgroup victim. They then heard a peer or adult model use low- and high-explicit responses, with or without a rationale. An explicit response refers to stating the behavioural rule and/or value — a strategy found to be effective in other forms of socialisation. When students were given an opportunity to respond, post-test interventions were more explicit than pre-test ones. However, third graders were more influenced by adult models and sixth graders more influenced by peer models. Rationales given by students also varied as a function of grade and model. The findings are an important starting point in informing programmes as to the words and the models most acceptable to students who are being asked to take a stand against bias and bullying.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".