From Hewers of Wood to Producers of Pulp: True Crime in Canadian Pulp Magazines of the 1940s
Bibliographic record
Abstract
In the 1940s, federal restrictions on the importation of U.S. publications spurred the growth of a Canadian pulp magazine industry, one branch of which was true crime. These cheap consumables, adorned with bawdy and violent cover imagery as well as sexually explicit advertisements, sometimes featured Canadian murder cases. True crime stories featured edgy dialogue and gumshoe argot but they remained within, and helped to define, the boundaries of heterosexuality, the racist moral hierarchies, and the certitude of explicable crime. Far from presenting authority figures in a dim light, Canadian true crime tales were written from the perspective of law men, the local police officers and the Mounties who doggedly gathered evidence and trailed unrepentant criminals. Writers took readers on journeys to morally dark places, particularly the remote north and the far west, where civilization along settled Euro-Canadian models had barely taken hold well into the twentieth century. Terrible murders, committed by ruthless criminals (typically Native men), threatened to rock the foundations of Canadian civilization but true crime reassured readers that the cops, the courts, and the gallows could and would always set it right. The industry declined by the 1950s, not on account of a moral-clean-up campaign but as a result of the pulp novel industry’s growth and the revocation of wartime importation bans.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.053 | 0.018 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".