Technical School in Toronto: Growing up in the Trades during the Second World War
Bibliographic record
Abstract
ABSTRACTThis article examines technical education in Toronto, Canada during the Second World War. Research on this topic reveals that there were enhanced links and patterns of interactions between the Toronto schools and the Canadian Armed Forces during the war. In particular, it was found that the war effort had a profound effect on technical education in Toronto because it strengthened links between the military and technical secondary schools, changed the curriculum and the school calendar, and helped attract technical students towards work in the armed forces and industry. The author examined these questions using primary sources from Toronto school archives and other City of Toronto archives.RÉSUMÉCet article s’intéresse à l’enseignement technique à Toronto (Canada) pendant la Deuxième Guerre mondiale. Cette recherche révèle qu’il y a eu des relations étroites et des modèles d’interactions entre les écoles torontoises et les Forces armées canadiennes durant la guerre. Entre autres, on a découvert que l’effort de guerre a eu des répercussions profondes sur l’enseignement spécialisé à Toronto. Il y a eu des rapprochements entre les militaires et les écoles techniques secondaires, on a modifié les programmes d’études et le calendrier scolaires et on a stimulé l’intérêt des élèves pour le travail dans l’armée et l’industrie. Cette recherche s’appuie sur des sources primaires provenant des archives des écoles torontoises et de la ville de Toronto.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".