Bibliographic record
Abstract
The changes that have taken place in the United States and Canadian immigration policies since the 1960s have changed the ethnic and racial profile of these states. The statistics and projections for the next 20-30 years show that ethnic and racial diversity in both the United States and Canada will only increase. Analysis of the transformation of the immigration policy of the USA and Canada of the fourth wave of immigration is of great scientific and practical value. Despite the Russian Federation’s hybrid aggression, Ukraine, being included in the world processes (such as modernization, democratization, globalization, etc.), should take into account the USA and Canada’s ways of implementing the immigration policy, as well as the role of the state in the control and regulation of immigration. We deliberately highlight the four periods in the history of the U.S. and Canadian immigration (although possible variations on the detail of its periodization). In our opinion, this is not derived only from the historical facts, or the entry into force (or the lapse of validity) of certain normative and legal acts, as confirmed by the studies on migration studies, but also corresponds to our research approach of cross-national comparison in the form of indirect (implicit) binary comparison with the use of a research strategy for most analogous systems. Keywords: Immigration, immigration policy, immigration waves, interethnic interaction
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".