Bibliographic record
Abstract
Abstract Although on a lesser scale than the United States, southern South America became a major receiving region during the period of mass transatlantic migration in the late 19th and early 20th centuries. Even as the white elites of most Latin American countries favored European immigration in the late 19th century, since in their eyes it would “civilize” their countries, it was the temperate areas closely tied into the Atlantic economy as exporters of primary products that received the bulk of European laborers. Previously scarcely populated lands like Argentina, Uruguay, and southern Brazil thus witnessed massive population growth and in some ways turned into societies resembling those of other immigration countries, such as the United States and Canada. This article concentrates on lands where the overwhelming majority of migrants headed, although it also briefly deals with Latin American nations that received significantly fewer newcomers, such as Mexico. This mass migration lastingly modified identity narratives within Latin America. First, as the majority of Europeans headed to sparsely populated former colonial peripheries that promised economic betterment, migration shifted prevalent notions about the region’s racial composition. The former colonial heartlands of Mexico, Peru, and northeastern Brazil were increasingly regarded as nonwhite, poor, and “backward,” whereas coastal Argentina, São Paulo, and Costa Rica were associated with whiteness, wealth, and “progress.” Second, mass migration was capable of both solidifying and challenging notions of national identity. Rather than crossing over well-established and undisputed boundaries of national identities and territories, migration thus contributed decisively to making them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".