Bibliographic record
Abstract
The US is the country of immigration, with almost 20 per cent of the world’s 260 million international migrants. The number two country with international migrants, Germany, has 12 million, a fourth as many as the almost 48 million foreign-born US residents (UN DESA, 2017). The US stands alone among industrial countries in having a quarter of its immigrants, almost 11 million, unauthorised (Passel and Cohn, 2018). President Trump made reducing illegal immigration a priority. Major migration issues today include the fate of programs such as DACA, what to do about Central American families who apply for asylum, and whether to build a wall on the Mexico-US border. In December 2018-January 2019, there was a partial shutdown of the federal government, the third in Trump’s first two years as President, because Congress failed to include $5 billion for the border wall in bills that fund DHS and other federal agencies. Meanwhile, Mexico agreed to issue humanitarian visas to Central Americans who enter the US and apply for asylum, so that Central American asylum seekers may wait in Mexico for US decisions on their cases
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".