Representing Crime in Words, Images, and Song: Exploring Primary Sources in the Murder of Mélina Massé, Montreal, 1895
Bibliographic record
Abstract
Les préjugés liés à la classe, au genre et à la religion ont conditionné les points de vue et l'information qui ont servi à décrire le meurtre survenu en 1895 d'une Montréalaise de 30 ans. La description du cas variait d'une source primaire à l'autre selon qu'on soit d'avis que la victime, Mélina Massé, avait été assassinée ou non par son époux prétendument violent. C'est en s'appuyant sur l'affaire ainsi que l'ont suivie divers journaux (y compris des sketches de scènes se déroulant en cour), les dépliants de deux comédies musicales et la réponse de l'avocat de la défense que cette étude révèle les tribunes dont le public se servait pour s'exprimer dans le Montréal du tournant du siècle. Plusieurs journaux de Montréal entretenaient clairement un préjugé fondé sur la classe, couvrant davantage les affaires de meurtre dans la classe ouvrière que dans la bourgeoisie. Dans l'ensemble, l'analyse donne à penser que l'information discordante provenant de ces sources traduit mal la réalité historique.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".