Bibliographic record
Abstract
Every winter settlers of the U.S. and Canadian Mountain West could expect to lose dozens of lives to deadly avalanches. This constant threat to trappers, miners, railway workers-and their families-forced individuals and communities to develop knowledge, share strategies, and band together as they tried to survive the extreme conditions of "avalanche country." The result of this convergence, author Diana Di Stefano argues, was a complex network of formal and informal cooperation that used disaster preparedness to engage legal action and instill a sense of regional identity among the many lives affected by these natural disasters. Encounters in Avalanche Country tells the story of mountain communities' responses to disaster over a century of social change and rapid industrialization. As mining and railway companies triggered new kinds of disasters, ideas about environmental risk and responsibility were increasingly negotiated by mountain laborers, at the elite levels among corporations, and in socially charged civil suits. Disasters became a dangerous crossroads where social spaces and ecological realities collided, illustrating how individuals, groups, communities, and corporate entities were all tangled in this web of connections between people and their environment. Written in a lively and engaging narrative style, Encounters in Avalanche Country uncovers authentic stories of survival struggles, frightening avalanches, and how local knowledge challenged legal traditions that defined avalanches as acts of god. Combining disaster, mining, railroad, and ski histories with the theme of severe winter weather, it provides a new and fascinating perspective on the settlement of the Mountain West.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 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".