The Paradise Syndrome: Environment, Boosters and Ranching along the Montana/Alberta Borderlands
Bibliographic record
Abstract
The history of cattle ranching on the Great Plains combines climate, grasslands, water, people and animals. Unfortunately, it also includes environmental missteps and catastrophes which has led to numerous negative histories that end on an environmental low-point. The resulting studies tend to blame rancher’s greed for damaging their environment, as opposed to finding an alternative explanation for their actions. Ranching histories, therefore, often follow a decline framework. The overarching message for these types of histories is that human interaction with the natural world is inherently negative. In short, historians have not been overly kind to cattle ranchers. This study complicates that history by examining why ranchers made the mistakes that they did and how they tried to correct them. It does not end on the environmental low point for the cattle industry but looks past it to consider what, if anything, was done to improve ranching methods. The discussion considers how ranching in Alberta and Montana started and why ranchers operated the way that they did. It argues that ranchers in both places started their operations with a fundamentally flawed understanding of the environment because the climate, grasslands and economic potential of the area had been a favorite topic for boosters during the 1870-1880s. What resulted was the importation of thousands of cattle and inappropriate ranching methods. After several years of temporary equilibrium in 1886/1887 a drought and hard winter occurred. By spring it was realized that thousands of cattle had frozen or starved to death where they stood. However, the disaster was not the end of the cattle industry in either Montana or Alberta. Ranchers on both sides of the border tried (often successfully) to adapt to their environment in order to continue in their industry. It is the recognition of a flawed understanding of the environment and then trying to adapt to it that forms the backbone of this work. This study is also a bioregional history of the nineteenth century Montana and Alberta borderlands. As such, it examines how people and governments responded to an environment and climate misrepresented by booster literature and government policy. The work itself is bioregional, yet also deals with broader ideas of nation building, borderland economics, the concept of natural disaster, and indigenous displacement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".