[PDF/Kindle] Crow Mary: A Novel by Kathleen Grissom
Bibliographic record
Abstract
Crow Mary: A Novel by Kathleen Grissom \n \n \n Download Book ➡ Link \n Read Book Online ➡ Link \n \n \n \n Crow Mary: A Novel \n Kathleen Grissom \n Page: 384 \n Format: pdf, ePub, mobi, fb2 \n ISBN: 9781476748481 \n Publisher: Atria Books \n \n \n Joomla books pdf free download Crow Mary: A Novel in English \n \n Overview \n The New York Times bestselling author of the book club classics The Kitchen House and Glory Over Everything returns with a sweeping and “richly detailed story of a woman caught between two cultures” (Sandra Dallas, New York Times bestselling author) inspired by the real life of Crow Mary—an Indigenous woman in 19th-century North America. In 1872, sixteen-year-old Goes First, a Crow Native woman, marries Abe Farwell, a white fur trader. He gives her the name Mary, and they set off on the long trip to his trading post in Saskatchewan, Canada. Along the way, she finds a fast friend in a Métis named Jeannie; makes a lifelong enemy in a wolfer named Stiller; and despite learning a dark secret of Farwell’s past, falls in love with her husband. The winter trading season passes peacefully. Then, on the eve of their return to Montana, a group of drunken whiskey traders slaughters forty Nakota—despite Farwell’s efforts to stop them. Mary, hiding from the hail of bullets, sees the murderers, including Stiller, take five Nakota women back to their fort. She begs Farwell to save them, and when he refuses, Mary takes two guns, creeps into the fort, and saves the women from certain death. Thus, she sets off a whirlwind of colliding cultures that brings out the worst and best in the cast of unforgettable characters and pushes the love between Farwell and Crow Mary to the breaking point. From “a tremendously gifted storyteller” (Jim Fergus, author of The Vengeance of Mothers), Crow Mary is a “tender, compelling, and profoundly educational and satisfying read” (Sadeqa Johnson, author of The Yellow Wife) that sweeps across decades, showcasing the beauty of the natural world, while at the same time probing the intimacies of a marriage and one woman’s heart.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.514 | 0.318 |
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".