Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the period of mass migration to the Americas, Argentina occupied second place, after the United States, in the number of immigrants who had come for settlement from overseas. Between 1820 and 1932, about 6.5 million immigrants arrived in Argentina.2 Although the United States received many more immigrants than did Argentina, the share of the foreign-born in the national population was much larger in the latter country than in the former. Another distinguishing feature of the immigration to Argentina was that most of its immigrants had come from Italy and Spain. In spite of the dominant role that southern Europeans played in the composition of immigration to Argentina, there was a signifi cant number of immigrants who also came from eastern Europe. Between 1857 and 1920 nearly 164,000 immigrants came to Argentina from the multiethnic Russian Empire and another 87,000 from Austria-Hungary.3 Over the next two decades, 182,000 immigrants came from Poland. Indeed, during the decade of the 1930s, the immigrants from Poland accounted for 58 percent of the net total of all newcomers to Argentina.4
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it