10. Engineering Gold Rushes: Engineers and the Mechanics of Global Connectivity
Bibliographic record
Abstract
Between 1848 and 1899, a series of explosive, short-term gold bonanzas struck California, Australasia, Southern and West Africa, and the Yukon Valley.These rushes unleashed waves of settlement, the forced displacement of indigenous peoples, and set in motion great transfers of capital, commerce, and technology around the world.Mining technologies and the broad-ranging expertise of geology, metallurgy, and hydrology were transferred overseas by the hundreds of thousands of migrants who flowed through great gateway cities such as San Francisco, Johannesburg, Melbourne, Dunedin, and Dawson.Enabling these currents of cross-border exchange were important network-builders and managers: engineers.Individually and collaboratively they redirected connectivity to new locales, integrated new expertise with existing networks, and created new institutions for supervising global industrial connections."After dwelling in a lowland of drowsy accomplishment for centuries," argued one American engineer in 1914, the "great gold discoveries" of the late nineteenth century "sprang" the industry "into gigantic activity.""This has been brought about", he continued, "by the growth of knowledge through science, invention, and engineering … on a scale never dreamt of before, and thus giving man an almost Aladdin's power." 1 In popular imagination, gold rushes sprang to life from moments of great luck.But actually their development depended upon engineers to manage the mass transfers of
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 | 0.001 | 0.003 |
| 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 | 0.014 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".