Beyond the Logic of Emblemization: Remembering and Learning from the Montréal Massacre
Bibliographic record
Abstract
Fifteen years ago, fourteen women were murdered at l’Ecole polytechnique (the School of Engineering) at the University of Montreal in Quebec, Canada. For those who lived in proximity to these murders, the details do not need to be recalled (for it is likely that they never left us). For others, this recollection alone will be insufficient to the substance of memory. So, in brief: in the early evening of December 6,1989, Marc Lepine, a twenty-five-year-old white man, entered a university building in the city of Montréal, armed with a semiautomatic rifle. He walked into a fourth year Mechanical Engineering class of some 60 students, ordered the men to leave—which they all did—and shot the remaining six women to death, screaming the accusation that they were a “bunch of feminists” (Rathjen and Monpetit, 1999, 10). He then walked through hallways and entered other classrooms, murdering eight more women and injuring thirteen others (nine women and four men, men who were shot presumably because they attempted to impede his rampage).Then, he killed himself. In the three-page note found on his body, but not released into public circulation for a year, he described the murders as a political act and blamed feminism for ruining his life. 1 These murders received widespread public attention across Canada. From grocery store lineups, to public memorial services, to campus classrooms, much was spoken and written about the killings and their significance, bringing to the fore debates about issues of violence against women in a manner that was unprecedented. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".