Bibliographic record
Abstract
This book seeks to provide an overview of the role of the railways in the age of imperialism and is based on the general assumption that rail transport was essential for the development of industry, capitalism and colonialism world-wide. It employs a variety of approaches to lay bare the interconnections between economic development and other aspects of society including the ideology underlying thought and action, while focussing on different parts of the world and different representations of trains, railways and railway building. Part I of the volume offers the economist’s approach to the “rationale” for colonial railways, the geographer’s investigation on how their construction affected colonisation and development in Canada and the cultural historian’s analysis of the military deployment of trains in the Sudan and the intense debate it generated in Britain. Part II consists of four literary case studies addressing the role of trains in the spreading of colonial ideology in French adventure literature for boys, Emilio Salgari’s take on railways in British India, the building of the Turkestano-Siberian Railroad (“Turksib”) and the ambivalent views on Central Asia’s modernisation as they emerge in a popular Soviet novel, and the way Joseph Conrad’s Nostromo textualises the multiple issues related to railway building, placing the Colonial Train at the core of capitalist exploitation in a fictional Latin American country.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".