Bibliographic record
Abstract
Webcast sponsored by the Irving K. Barber Learning Centre and hosted by the Asian Canadian and Asian Migration Studies (ACAM) program as part of the honouring of Dr. Henry Sugiyama. An 87-year-old Canadian doctor of Japanese ancestry, Dr. Sugiyama is the first student in a new UBC program on Asian Canadian studies. This is a special lecture presentation from History 483 - Asian Migrations to the Americas - taught by UBC History Professor Dr. Henry Yu. Migration from Asia was, and remains, a formative influence on the social, economic, cultural and political life of the Americas. This course will explore the history of migration from Asia to, and throughout, Canada, the United States and Mexico from the late 19th to the early 21st century. It will examine the impact of migration from different parts of Asia on local, national and transnational communities. Through a variety of readings and engaged seminar discussions, we will explore a range of topics including contested conceptions of Asian-ness, the relationship between migration and indigeneity in the Americas and how perceptions of arriving migrants changed over time. We will contribute our own understandings and interpretations to historiographical discussions as well as contemporary debates about migration from Asia to, and throughout, the Americas.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.243 | 0.060 |
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".