Foreign Workers: Information on Selected Countries' Experiences
Bibliographic record
Abstract
A letter report issued by the Government Accountability Office with an abstract that begins "The opportunity for employment is an important magnet attracting immigrants, including unauthorized immigrants, to countries. The policies and practices used by other countries to manage foreign workers, including actions to limit illegal immigration and to reduce the employment of unauthorized foreign workers, have been shaped by country-specific economic, demographic, and political factors. Immigration reform is a matter of continuing debate in the United States. This report examines selected countries' (1) programs for admitting foreign workers; (2) efforts to limit the employment of unauthorized foreign workers; and (3) programs for providing unauthorized immigrants with an opportunity to obtain legal status, referred to as regularization. To address these objectives, we examined reports from foreign countries, intergovernmental organizations, and research organizations. We also interviewed government officials and experts from 8 countries--Australia, Belgium, Canada, France, Germany, Spain, Switzerland, and the United Kingdom--and surveyed 6 other countries. We selected these countries based on their net immigration rate, population size, membership in the Organisation for Economic Co-operation and Development or World Bank classification as high income, range of immigration policies, and geographic location."
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".