Bibliographic record
Abstract
The purpose of our collaborative research is to explore effective strategies to ensure student safety in elementary schools. Student safety includes both physical safety, prevention of physical violence, abuse, or bullying (DPHCS, n.d.); and psychological safety, establishing well-being, belonging in classrooms and support for emotional and mental health. Findings suggest misunderstandings, microaggressions, racism, and physical violence directly undermine feelings of belonging (Cohen, 2021), with a wide range of school violence leaving students vulnerable to a negative learning environment and an adverse impact on growth (NASP, 2023). Elementary teachers should collaborate with principals, school boards, and parents in fostering positive behavioural learning and the continuous support of Bill 13 of the Accepting Schools Act (DPCDSB, 2012). Further research on this topic could enact new safety regulations within schools, with an emphasis on psychological safety and better implementation of physical safety protocols. Our recommendations will support elementary educators as they recognize the ineffectiveness of punitive negative reinforcement, and the importance of necessary precautions and getting involved in protecting the school community.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.162 | 0.051 |
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".