Bibliographic record
Abstract
River House is about a young woman facing opposing urges between land, family, familiarity, and liberation. At the heart of it is my lifetime in the central Oregon desert and my life as an international river guide. The framework of this book is the building of a log house with my father on our ranch. On the surface the work is a discussion of what it means to build a home, perfect for the place it sits and sensitive to its desert environs. I flash to the past, the future, and beyond property lines. I explore community, politics, fire and water. I explore what binds my father and me and what we are willing to endure for place. In a quarter century I have witnessed change on grand social and environmental scales. Central Oregon has become the trendy new west full of people who abandoned their own places for this better place. I write to preserve the open and the harsh desert I grew up in. I hope to help people understand the human and animal communities that give definition to this desert on the hottest wavering days of summer and the flattest cold days of winter. This is a long look beyond the white plastic fences around a single horse and giant house that characterize the modern ranchette. I write for people who live in the desert for privacy. I write for people who live in the desert for community. I write for the environment and an ecosystem that I am a part of.
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 | 0.000 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.572 | 0.289 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".