Bibliographic record
Abstract
The term "weapon " has broad meaning. It includes everything from firearms and knives to commonplace objects like pen knives, laser pointers, aerosol/pump sprays and elastic bands. The design, intended use and specific circumstances will often dictate whether an object is viewed as a weapon or not. Section 2 of the Criminal Code of Canada defines "weapon " as follows: • Anything used or intended for use in causing death or injury to persons whether designed for that purpose or not or • Anything used or intended for use for the purpose of threatening or intimidating any person. • Firearms are included in this definition. • Prohibited weapons include gas discharge devices, numchuks, throwing stars, electronic "zappers", brass knuckles, silencers, and switchblades. 1. (a) Firearms or prohibited weapons will not be permitted on school property or at any school sponsored event or activity. (b) Any student who threatens to use, or is found in possession of, a firearm or prohibited weapon will face disciplinary action by the Principal. Consequences may include maximum-term suspension and recommendation to the Board for expulsion.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.177 | 0.078 |
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".