Bibliographic record
Abstract
Bullying in school has become a topic of international concern over the last 10–20 years. Starting with research in Scandinavia, Japan and the UK, there is now active research in most European countries, in Australia and New Zealand, Canada and the USA, and Japan and South Korea (Jimerson et al ., 2010; Smith et al ., 1999). This chapter discusses what we mean by ‘bullying’; summarises some recent research findings on the nature of bullying; discusses the results of large-scale, school-based interventions; and raises issues for future research and practice. Definitions of bullying What do we mean by bullying ? Although there is no universally agreed definition, there is an emerging consensus in the western research tradition that bullying refers to repeated aggressive acts against someone who cannot easily defend themselves (see Olweus, 1999; Ross, 2002). A similar definition, though perhaps with broader connotations, is that bullying is a ‘systematic abuse of power’ (Rigby, 2002; Smith and Sharp, 1994). Although the two criteria of repetition, and power imbalance, are not universally accepted, they are now widely used. Bullying, by its nature, is likely to have particular characteristics (such as fear of telling by the victim), and particular outcomes (such as development of low self-esteem, and depression, in the victim). The relative defencelessness of the victim implies an obligation on others to intervene, if we take the democratic rights of the victim seriously.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".