Bibliographic record
Abstract
Canada is renowned as a country that welcomes thousands of immigrants every year and is praised as a success in multiculturalism.But Canada was not always so welcome to immigrants and it has only been 40 years since Canada instituted a non-discriminatory, points-based immigration system.Though Canada was always "multicultural," the demographic nature of the country took a marked change after 1900.The federal government invited hard-working immigrants from around the world to build the new country.However, the presence of so many Asian immigrants upset many segments of white society in British Columbia.This paper looks at how Canadian politicians justified an exclusionary immigration policy to solve the "problem" of Japanese immigration.This will focus primarily on the Lemieux mission, which was a Canadian diplomatic mission in 1907 aimed at restricting Japanese immigration to Canada. Background to the Lemieux MissionDuring the fi rst decade of the twentieth century, Canada experienced the fi rst major economic and demographic boom in its young history.Sir Wilfrid Laurier, Prime Minister from 1896-1911, famously promised that "the 20 th century will belong to Canada" and he invited hard-working immigrants from Europe to help him build that dream.There was a hierarchy in the type of immigrant sought.As Canada was a member of the British Empire, white, Protestant Britons were preferred.Northern
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.049 | 0.018 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".