How prevalent is contextual information in research on school bullying?
Bibliographic record
Abstract
Empirical articles on school bullying need to give contextual details of the study, including on participants (number, age, gender), the country in which data was gathered, and the year in which it was gathered. We argue that these are important aspects of information, and that country and year of data collection cannot be inferred unambiguously unless they are explicitly stated. We report an analysis of contextual information on a sample of 201 articles, from 1976 to 2015, on school bullying. The great majority of studies gave information on number and age of participants, and most on gender balance. Most also gave explicit information on the country in which data was gathered. However only about one quarter of articles gave information on the date (year) in which data was gathered. For those that did, the average gap from data gathering to publication was 4 years, with a range of 1 to 11 years. We argue that the date of data collection is an important historical aspect, as many societal changes, even over a period of a few years, can impact on prevalence and nature of bullying. We recommend that besides participant and country information, year of data collection is routinely given in empirical articles on school 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 | 0.089 | 0.334 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.025 | 0.039 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".