Bibliographic record
Abstract
I mmigration is a major topic of discussion today. The United States, Canada, Europe, Australia, Africa, Latin America, or Asia—it makes no difference. In the postindustrial states of Europe and North America, which receive many of today’s immigrants, the media play an extensive role in publicizing various strands of the current debates. Television, radio, the Internet, blogs, and newspapers continually include stories about immigrants, undocumented “illegal” aliens, refugees, and asylum seekers. Popular magazines as well as serious journals cover the topic. There are photographs and descriptions of overcrowded refugee camps where inhabitants lack the most basic facilities, like running water and sanitation. Television and radio newscasts recount the misery of refugees in Darfur; the victims of earthquakes in Haiti, Pakistan, China, and Turkey; the devastation and dislocation following a tsunami in Indonesia; and Pakistanis and Afghanis fleeing from the Taliban. Articles depicting the plight of refugees seeking to escape from civil wars in Africa and Asia appear regularly, as do stories about immigrants who leave their home country simply to escape starvation or grinding poverty and who are trying to find a decent, stable life for themselves and their families. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 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".