CARMELA PATRIAS. Jobs and Justice: Fighting Discrimination in Wartime Canada, 1939-1945.
Bibliographic record
Abstract
There is conventional wisdom that World War II effected a paradigm shift in Canada (and elsewhere) in terms of human rights and race relations; fighting a racist enemy allegedly enlightened the majority population, allowing them to recognize, and hence to correct, issues of discrimination and inequality. Jobs and Justice joins an emerging literature challenging this misunderstanding. Carmela Patrias has written an interesting, useful, and important book. Her stated goal is “to examine the nature and extent of racist employment discrimination during the Second World War,” and to show that “state officials colluded with racist employers and workers” (p. 5). In the course of fulfilling this goal Patrias offers a thorough description of the kind of discrimination faced by racialized minorities in Canada, and in fact shows that racist restrictions actually increased during the first half of the war. The National Selective Service (NSS), a federal agency established in 1942 to direct civilians into appropriate jobs to support the war effort, acceded to employers' prejudices and itself discriminated in channeling racialized minorities into poorly paid and stereotypical positions, or leaving them unemployed in a situation crying for their labor. Even the military participated, maintaining restrictions on recruitment and rank until well into the war.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".