Bibliographic record
Abstract
After World War II, hundreds of thousands of Italians emigrated to Toronto. This book describes their labour, business, social and cultural history as they settled in their new home. It addresses fundamental issues that impacted both them and the city, including ethnic economic niching, unionization, urban proletarianization and migrants’ entrepreneurship. In addressing these issues the book focuses on the role played by a specific economic sector in enabling immigrants to find their place in their new host society. More specifically, this study looks at the residential sector of the construction industry that, between the 1950s and the 1970s, represented a typical economic ethnic niche for newly arrived Italians. In fact, tens of thousands of Italian men found work in this sector as labourers, bricklayers, carpenters, plasterers and cement finishers, while hundreds of others became contractors, subcontractors or small employers in the same industry. This book is about these real people. It gives voice to a community formed both by entrepreneurial subcontractors who created companies out of nothing and a large group of exploited workers who fought successfully for their rights. In this book you will find stories of inventiveness and hope as well as of oppression and despair. The purpose is to offer an original approach to issues arising from the economic and social history of twentieth-century mass migrations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".