Bibliographic record
Abstract
McNamara, Amy. Lovely, Dark and Deep. New York: Simon & Schuster Books for Young Readers, 2012. Print. Lovely, Dark and Deep is Brooklyn-based poet and photographer Amy McNamara’s debut novel. The title, well-chosen and from the last stanza of Robert Frost’s poem Stopping by Woods on a Snowy Evening – The woods are lovely, dark and deep. But I have promises to keep, And miles to go before I sleep, And miles to go before I sleep. captures the main character’s mindset perfectly as she distances herself, both physically and emotionally, from her friends and family after a tragic car accident. Wren Wells, whom her mom still calls by her original name, Mamie, has moved in with her sculptor father, to an isolated house by the sea in rural Maine. Dealing with immense guilt and grief after surviving the crash that claimed the life of her high school boyfriend Patrick, Wren spends her days yearning to be left alone. Her father, a famous artist out of touch with his daughter’s life, is at a loss for how to help, and her well-meaning and understandably worried mother asks frequently about when Wren will go to college. Wren’s friends from her seemingly previous life struggle to understand the changes she is going through and in the process feel alienated. To pass the never-ending days, Wren runs - a lot. She runs to get away and be alone with her thoughts. And during this time, Wren meets Cal, who is hiding away from his own troubles. The chemistry between them, and the glimpses of joy she feels while with Cal is unnerving to Wren, who believes that she should feel guilty forever about Patrick, who was not given a second chance. Wren needs to make a choice – move on and try again or be lost forever. The character of Wren is not endearing to the reader; she is self-absorbed and manic, and selfish to the point of frustration. Her actions and of those around her often seem unauthentic. The plot line is slow, yet McNamara’s beautiful prose makes it bearable. Wren is, however, a young, depressed girl coping with deep grief and the story may resonate with young women who have experienced similar situations. Recommended with reservations: 2 stars out of 4 Reviewer: Debbie FeisstDebbie is a Public Services Librarian at the H.T. Coutts Education Library at the University of Alberta. When not renovating, she enjoys travel, fitness and young adult fiction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.083 | 0.061 |
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".