Bloody Wednesday in Dawson College - The Story of Kimveer Gill, or Why Should We Monitor Certain Websites to Prevent Murder?
Bibliographic record
Abstract
The article deals with the Dawson College Massacre, focusing on the story of Kimveer Gill, a 25-year-old man from Laval, Montreal who wished to murder young students in Dawson College. It is argued that the international community should continue working together to devise rules for monitoring specific Internet sites, as human lives are at stake. Preemptive measures could prevent the translation of murderous thoughts into murderous actions. Designated monitoring mechanisms of certain websites that promote violence and seek legitimacy as well as adherents to the actualization of murderous thoughts and hateful messages have a potential of preventing such unfortunate events. Our intention is to draw the attention of the multifaceted international community (law enforcement, governments, the business sector including Internet Service Providers, websites' administrators and owners as well as civil society groups) to the shared interest and need in developing monitoring schemes for certain websites, in order to prevent hideous crimes.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".