Chinese Railroad Workers, the Transcontinental Railroad, and the Indispensability of Immigration to America
Bibliographic record
Abstract
Asian migration to America began with Chinese railroad workers on the transcontinental railroad (1862-1869). Their labor saved the foundering Central Pacific Railroad, challenged by building a rail line through the Sierra Nevada. By mid-1864 only 50 miles of track had been laid, grueling work that dissuaded its white workforce from going any further. To save the railroad 50 Cantonese workers were hired in early 1864 from neighboring mines to lay rail through forests, canyons, and granite mountains. High explosives, rockslides, cave-ins, and winter avalanches were constant dangers. The trial worked so well that thousands of Chinese joined the effort, many from the rural districts surrounding Guangzhou (Canton). The wages, less than half of that paid to white workers, were beyond the imaginations of subsistence farmers escaping abject poverty, plague, and famine. A good proportion of their earnings were remitted to families back home. As many as 20,000 may have worked on the railway. The death toll was staggering, estimated in the thousands. After Promontory Summit in 1869, Chinese were in great demand, building scores of rail lines throughout the country and Canada. Just 13 years later rising anti-Asian sentiment led to the passage of the Chinese Restriction Act of 1882 that for the first time barred a racial group from American shores. But they opened America to Asian immigrants that includes today's Asian surgical community, which owes its present-day success to the hardworking forebears that created a global country with ribbons of steel rail.
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 | 0.000 | 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 | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".