Bibliographic record
Abstract
Abstract The knowledge base on bullying within school and working contexts has matured to the extent that researchers and practitioners are developing a deeper understanding of this complex social relationship problem. Although controversies still exist, evidence to date provides estimates of the prevalence of bullying, risk factors for bullying, antecedents of bullying, and theoretical models explaining bullying behavior and experience. There is little doubt that bullying in all its forms can have severe negative impacts on those involved in the bullying situation. As a result, it is important to establish coherent and evidence-based approaches to preventing bullying behavior in schools and workplaces. In contrast, the development and evaluation of bullying interventions has not received the same level of support. Both in school and working contexts, there are examples of preventative approaches, but either these are espoused and not directly evaluated, or, where evaluations exist, data is limited in providing definitive answers to the success of an approach. An increasingly dominant voice advocates the creation of policies and laws for preventing workplace bullying. However, the usefulness of policies and laws on their own in reducing bullying is questionable—especially if they are developed with a quick-fix mentality. Trying to prevent such a complex social phenomenon requires an integrated program of actions necessitating significant investment over a prolonged period of time. Stakeholder engagement is paramount to any intervention. Ultimately, schools and workplaces need to try to develop a culture of dignity, fairness, respect, and conflict management which pervades the institution. Challenges remain on how to create such interventions, whether they are effective, and what impact societal values will have on the success of bullying prevention strategies.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".