Bibliographic record
Abstract
Building Nations from Diversity explores the question of whether the Canadian "mosaic" has differed from the American "melting pot" and provides an informative comparison of both countries' historical and present-day similarities and differences. Garth Stevenson examines the origins of Canada and the United States and their past experiences with incorporating selected immigrant groups, particularly Irish, Chinese, and Jews. Establishing the foundational ways in which they placed new groups within their societies, Stevenson then outlines how the US and Canadian systems developed immigration policy and handled difference, detailing their treatment of "enemy aliens" during both world wars, their experience with minority languages, and recent Islamophobia. He also studies the introduction of multiculturalism into the lexicon and policy of the two countries and presents a nuanced analysis of how its meaning is understood differently on opposite sides of the border. An accessible and illuminating work, Building Nations from Diversity highlights the substantial differences between the US and Canada but ultimately concludes that they are more similar than most realize and are probably becoming more alike.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".