3 Playgrounds for Travellers: Infrastructure, Environment, and Spatial Transformation in the Americas, 1880s–1920s
Bibliographic record
Abstract
Peters 3 Playgrounds for Travellers:Infrastructure, Environment, and Spatial Transformation in the Americas, 1880s-1920s 1 IntroductionCanada, the United States, and Brazil are the three largest countries in the Americas.1 Given their continental size, it is not surprising that, as historians Jay Young, Ben Bradley, and Colin M. Coates noted for Canada some years ago, notions of national history as an "epic struggle to penetrate the wilderness, capture resources, and consolidate the country through improved transportation live on in the popular imagination".2 The same is true for the United States and Brazil.In the three countries, railways, roads, and other transportation infrastructures played an important role in state-led projects of national integration and modernization during the late nineteenth and much of the twentieth centuries.3 The expansion of transportation infrastructures was also crucial to the development of travel and tourism, whose promotion, in turn, was a centrepiece of nationalistic cultural politics.Across the Americas, the construction of railways, roads, lodging structures, and other tourism facilities significantly transformed natural environments and helped turn remote areas into accessible landscapes and major tourist destinations.This chapter, focusing on the late nineteenth century and the 1920s, examines processes of spatial transformation that were connected to the creation of recreational infrastructures for train travellers and automobilists in North America and Brazil from a transnational perspective.In Canada and the United States, the I have published some parts of this chapter in M.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".