“They think it’s totally fine to talk to somebody on the internet they don’t know”: Teachers’ perceptions and mitigation strategies of tweens’ online risks
Bibliographic record
Abstract
Teachers play a key role in educating children about digital security and privacy. They are often at the forefront, witnessing incidents, dealing with the consequences, and helping children handle the technology-related risks. However, little is reported about teachers’ lived classroom experiences and their challenges in this regard. We conducted semi-structured interviews with 21 Canadian elementary school teachers to understand the risks teachers witness children aged 10–13 facing on digital media, teachers’ mitigation strategies, and how prepared teachers are to help children. Our results show that teachers regularly help children deal with digital risks outside of teaching official curriculum, ranging from minor privacy violations to severe cases of cyberbullying. Most issues reported by teachers were the result of typical behaviours which became risky because they took place over digital media. We use the results to highlight implications for how elementary schools address digital security and privacy.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".