To ban or not to ban – domestic and international experiences of restricting mobile phone ban use in schools
Bibliographic record
Abstract
Perspectives on the use of telecommunication devices in educational institutions vary internationally. France implemented restrictions in 2017, followed by China in 2018 and Canada in 2019, with several other countries - including Denmark, Sweden, and England - subsequently adopting similar measures. Evidence suggests that banning mobile phones in schools produces mixed outcomes regarding students’ academic achievement, mental well-being, and exposure to cyberbullying. In Hungary, such regulation took effect on 1 September 2024 under Government Decree No. 245/2024 (VIII. 8.), which designates telecommunication devices as prohibited or restricted items within schools. The primary rationale is that mobile phone use distracts students from learning and facilitates cyberbullying. The decree applies to mobile phones, tablets, laptops, and smartwatches, while allowing occasional use with explicit permission from a teacher or principal, provided the purpose and duration are clearly defined. This study presents findings from a quantitative survey conducted among secondary school teachers regarding the local implementation of these restrictions and their perceived impact on classroom activities and break-time interactions. Drawing on the responses of 1,198 teachers, the paper offers a summary of the first year’s experiences following the enforcement of the regulation.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".