The Montreal Massacre: A Story of Membership Categorization Analysis
Bibliographic record
Abstract
The Montreal Massacre: A Story of Membership Categorization Analysis adopts an ethnomethodological viewpoint to analyze how the murder of women by a lone gunman at the Ecole Polytechnique in Montreal was presented to the public via media publication over a two-week period in 1989. All that the public came to know and understand of the murders, the murderer, and the victims was constituted in the description and commentaries produced by the media. What the murders became, therefore, was an expression of the methods used to describe and evaluate them, and central to these methods was membership category analysis -- the human practice of perceiving people, places, and events as members of categories, and to use these to explain actions. This is evident in the various versions comprising the overall story of the Massacre: it was a crime; it was a tragedy; it was a horror story. The killer's story is also based on his own categorial analysis (he said his victims were feminists). The media commentators formulated the significance of the murders in categorial terms: it implicated a wider problem, that of violence against women, and thus the reasons for the murders were shown to be categorial matters. As a contribution to sociology, and as a demonstration of the significance of ethnomethodology for understanding social life, the book reveals the methodical and particularly categorial character of how sense is made of events such as this and how such methodical and categorial resources are central to human interaction.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.049 | 0.035 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".