The Three Ages of Algerian Emigration.1 By Abdelmalek Sayad.
Bibliographic record
Abstract
Global migration continues to define our times, with 258 million individuals living outside their country of birth in 2017, including nearly 44 million in the United States alone.2 These immigrants (and their children) are reshaping the economic, social, cultural, and political life of their host societies, as well as creating unprecedented levels of ethnic, racial, and religious diversity in the nations and communities where they live. Now more than before, with xenophobia and anti-immigrant and anti-refugee politicking on the rise in the United States and many European countries, there is a need and demand to better understand the causes and consequences of international migration. These demographic and political realities are helping to inspire new graduate programs focused on international migration, refugees and forced migration, and diasporic, ethnic, and multicultural relations. A quick online search turned up more than 40 master’s programs and a few doctoral programs in these areas, the majority at European and Canadian universities and many created in recent years. These programs are interdisciplinary in nature, drawing on theories, research methods, and empirical data from different academic disciplines.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.014 |
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".