Bibliographic record
Abstract
Despite decades of official denial, modern Germany has always been a country of immigration. From Poles migrating to the Ruhr in the late nineteenth century, to German refugees and expellees after World War II, to Italians and Greeks in the 1950s, to ethnic Germans from the former Soviet Union and refugees from Bosnia in the 1990s, the country has a long history of attracting newcomers. In fact, according to the recently released 2011 census data, approximately 19 percent of the Federal Republic’s population of around 80 million has a “migration background.”1 Of course, this national average masks substantial variation at the state or city level—places like Hamburg, Berlin and Baden-Württemberg have shares of residents with such a background of a quarter or more, whereas the eastern Länder have proportions under 5 percent. This sizeable population is also very different than a generation ago—increasingly rooted and diverse: 60 percent of this group has German citizenship and about half of this subgroup was born in Germany. Regarding countries of origin or ancestry, 17.9 percent have origins in Turkey, 13.1 percent in Poland, and about 8.7 percent in both Russia and Kazakhstan.
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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.207 | 0.098 |
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".