Normative Considerations in the Aftermath of Gun Violence in Schools
Bibliographic record
Abstract
Abstract Gun violence in American and Canadian schools is an ongoing tragedy that goes substantially beyond its roots in the interlocking emotional and behavioral issues of mental health and bullying. In light of the need for effective policy development, Dianne T. Gereluk, J. Kent Donlevy, and Merlin B. Thompson examine gun violence in schools from several relevant perspectives in this article. The authors consider the principle of standard of care as it relates to parents, teachers, and community members in a particular school's context. They posit that normative principles may provide a procedural mechanism appropriate for policymakers and practitioners when contemplating and implementing heightened security measures. Finally, they propose Rawlsian reasonableness as an effective and deliberative democratic process that reduces emotional, reactive responses to school shootings. Through these overlapping concepts, the authors advocate for purposeful discussions regarding gun violence in schools based on the unique pragmatic, educational, social, political, and contextual circumstances of individual schools and their surrounding communities.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.038 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| 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 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".