Cornerstone of that New Imperialism: Us Mining Engineers and Labor Management in Southern Africa, 1890–1910
Bibliographic record
Abstract
At a 1907 meeting of the South African Association of Engineers, held in the offices of the Transvaal Chamber of Mines in Johannesburg, the Association's vice president, a mechanical engineer from Ontario, Edgar Laschinger, reflected on the first two decades of gold mining on South Africa's Witwatersrand Basin. 1 Given the sheer scale of the work involved in extracting and pro cessing the Rand's ore, Laschinger would have surprised no one in the room when he observed, "One of the greatest questions in South Africa has been and is, labour."Far more surprising, at least to historians, is his argument that "to an overwhelming extent, this is an engineering prob lem."The Canadian engineer's remark suggests how essential highly mobile professional engineers were to both the "machinery" and " human machinery" of the world's foremost gold mining region, and his career encapsulates many of the themes of this book.2 As Beatty and Solares show in chapter 1 in this volume, engineers like Laschinger were newly professionalized, mobile, oriented toward specific outcomes like cost-efficient ore extraction, and, as this chapter demonstrates, central actors in a dramatic episode of imperialism and economic globalization.Perhaps most significantly, Laschinger and the other engineers active on the Rand are a key example of how engineers' technical and cultural authority-in this case, over the Earth and over certain humans-was inseparable from white supremacy.This chapter will show how the way engineers approached the so-called labor prob lem in the mines 4 CORNERSTONE OF THAT NEW IMPERIALISM: US
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".