Bibliographic record
Abstract
With a July 2012 Pacific Northwest road trip undergirding this narrative nonfiction woven from three strands (the present, past, and contemplations on science, nature, and mythology), the Colorado author's husband and aging Yellow Labrador join her during the Summer of Fire as she returns to places of wild beauty where, in her twenties, she lived off-the-grid, rock-climbed, and engaged in ecofeminist campaigns to save wolves and wilderness from destruction. They are on their way to the San Juan Islands in the hope of seeing whales. But the road trip through Wyoming, Montana, Idaho, Washington, and Oregon also forces this former veterinary student to face the events of March 19, 1995. That first Sunday of Spring Break eighteen years ago resulted in an accidental death of her ex-partner's German shepherd, who was like a child to them, and nearly took their lives as well. The incident at Oregon's Crooked River Gorge was the defining moment of the young animal-lover's life; there was Before the gorge and there is After. There are still things that happened there that she cannot face, including what happened just after the dog fell to his death. Was she reckless that evening, or merely innocent? And does innocence get us off the hook for actions that result in irreversible loss and suffering--how does one forgive oneself for the sin of carelessness? It's time to face the unfaceable and return to the abyss. But it's also time to work out these complicated relationships with the other animals, with romantic partnerships, career aspirations, and with the untrusting self. It's time to take a close look at terrifying questions of life and death and irreversibility, control vs. responsibility, and what it means to be human and terribly, terribly fallible. What it means to not find what one had hoped for but to discover improbable serendipities just as redemptive.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".