Bibliographic record
Abstract
Two thirds of the 272 million international migrants in 2019 were employed in the destination country. Demographic and economic inequalities between countries, combined with globalization that reduced barriers to migrants, were expected to continue to increase the number of international migrant workers. Covid-19 closed many national borders to non- essential travelers, with limited exceptions. Seasonal farm workers were one of the notable exceptions, suggesting that many governments do not expect local workers to fill seasonal farm jobs despite record-high unemployment rates. For agriculture, the longer term effects of the pandemic include faster mechanization, more guest workers, and rising imports. Responses are likely to vary by commodity and be shaped by government policies. This article provides a review of the distribution and activities of the world’s 164 million international migrant workers in 2017, including the 111 million in high-income countries. The analysis focuses on the North American migrant worker and the differences between their integration in the agricultural industries. American agricultural systems are integrating in the sense that Canadian blueberries, Mexican avocados and U.S. meat trade freely, but the farm workforces in each country are increasingly Mexican.
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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".