Bibliographic record
Abstract
When her husband dumps her for an old girlfriend and sets all of Peachland, South Carolina, gossiping, Janey Daniels has to get away—far away—for a "sabbatical" year. She flees to Burlington, Vermont, home of Aunt May, her mother's only living relative. There she adopts Beulah, a Labrador puppy in training to become a companion dog for the blind. Not for a moment does Janey suspect that this "year of the dog" will change her life forever. Shelby Hearon is an acknowledged master at illuminating the nuances of relationships. In Year of the Dog, she explores the surprising ways that the heart heals after a betrayal. While Janey is training Beulah, Beulah leads Janey to a new love, James Maarten, a smart, "fidgety" teacher they meet at the dog park. As Janey soon discovers, James has suffered a betrayal of his own that makes it hard for him to open up and trust her with even the smallest details of his past. While Janey tries to help James, she also reaches out to her enigmatic Aunt May, a retired librarian reputed to be the friend, perhaps even the lover, of popular mystery writer Bert Greenwood. When Janey attempts to solve the twin mysteries of why her great aunt has distanced herself from the family—and what her true relationship is with Bert Greenwood—Beulah provides the clues that lead Janey to uncover the secrets of her aunt's life. By the time Beulah's stay with Janey comes to an end, the people whose lives she's linked will discover that healing and reconciliation can come in the most unexpected ways.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.213 | 0.052 |
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".