Agents of Globalization: Shipping Companies, Labor Recruiters, and Landlords in the Making of the European Exodus, 1870-1930
Bibliographic record
Abstract
This dissertation shows how businesses including shipping companies, labor recruitment agents, and landed elites—“agents of globalization”—decisively shaped global mobility in an era of nominally “free” and unregulated migration. From South Carolina and Argentina to Saskatchewan and Australia, businesses tried desperately to court immigrants, resorting to subsidies and marketing campaigns, or, failing these, deceit and coercion. Recruiters were invariably aided by shipping companies, which agglomerated during the 1870s into a handful of globe-straddling, modern corporations like the British Cunard or the German Hamburg-America Line. Never simply neutral conduits of global interconnectivity, steamship lines indiscriminately fomented migration to countless destinations, which delivered profits far exceeding those on freight. Across Europe, meanwhile, the deluge of shipping agents and foreign recruiters provoked a backlash from commercial farmers, who had been buffeted by world markets and now faced a depleting labor force and rising wages. The net result was an international migration regime quite unlike our own. Ours is an age of chronic structural unemployment in the richest countries, in which liberalized immigration policies are consequently a hard sell. In 1900 by contrast the economies of the Americas, Australia, and New Zealand wrestled with an inexhaustible appetite for labor power. Simply put, even unskilled labor was a desperately sought commodity in a world awash in land and capital.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".