School bullying before and during COVID‐19: Results from a population‐based randomized design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examined the impact of COVID-19 on bullying prevalence rates in a sample of 6578 Canadian students in Grades 4 to 12. To account for school changes associated with the pandemic, students were randomized at the school level into two conditions: (1) the pre-COVID-19 condition, assessing bullying prevalence rates retrospectively before the pandemic, and (2) the current condition, assessing rates during the pandemic. Results indicated that students reported far higher rates of bullying involvement before the pandemic than during the pandemic across all forms of bullying (general, physical, verbal, and social), except for cyber bullying, where differences in rates were less pronounced. Despite anti-Asian rhetoric during the pandemic, no difference was found between East Asian Canadian and White students on bullying victimization. Finally, our validity checks largely confirmed previous published patterns in both conditions: (1) girls were more likely to report being bullied than boys, (2) boys were more likely to report bullying others than girls, (3) elementary school students reported higher bullying involvement than secondary school students, and (4) gender diverse and LGTBQ + students reported being bullied at higher rates than students who identified as gender binary or heterosexual. These results highlight that the pandemic may have mitigated bullying rates, prompting the need to consider retaining some of the educational reforms used to reduce the spread of the virus that could foster caring interpersonal relationships at school such as reduced class sizes, increased supervision, and blended learning.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it