The Three Ages of Algerian Emigration.1 By Abdelmalek Sayad.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it