Bibliographic record
Abstract
were shot and killed by Marc Lépine on December 6, 1989, it was not as persons with names that they were murdered.They were killed because they were treated as instances of a category: feminists.Indeed, part of the response to the murders consisted of recovering the identities of the victims as particular persons with individual lives, rather than as incumbents of a category.Newspapers and other media printed their names, often in a framed or boxed list.Short obituaries were provided of each one.The heartfelt comments of family members were reported as well.We remember the women each year at memorial services on university campuses and elsewhere.It was initially out of a concern and involvement with gender politics, both on and off campus, that one of us was led to clip the papers as they appeared and to put the clippings in a file.But what brought the clippings out of the file was categories: "You're women.You're going to be engineers.You're all a bunch of feminists."This immediately made the massacre a topic for us given our sociological, that is to say, ethnomethodological, interest in categories.Moreover, the course of action that became formulated as the Montreal Massacre was social-ized from the beginning.That is, in its formulation and execution, and in the reaction to it, it was produced as a societal phenomenon.It was done as and through sociological analysis, both of the lay and more-or-less professional varieties.That included seeing it as having roots in, and consequences for, the social structure itself.Treating the massacre as produced through the sociological analysis of the parties to the event gave it, then, a second relevance for us as ethnomethodologists.For the practices of sociological inquiry form a cardinal, not to say primordial, topic of inquiry for ethnomethodology.vii
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.648 | 0.454 |
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".