Bibliographic record
Abstract
Bobet, Leah. Above. New York: Arthur A. Levine Books-Scholastic, 2012. PrintThis Young Adult urban fantasy novel takes place in present-day Toronto, Canada. The main character is Matthew, a teenager growing up in an underground, secret community known as Safe. This community was founded by Matthew’s guardian, Atticus, for disabled outcasts and people with abnormalities. For example, Atticus has claws for hands, and Matthew has scales. In this underground community, Matthew is Teller, which means that he collects and remembers the stories of different individuals living in Safe. Matthew is in love with the traumatized girl Ariel, who can shape-shift into a bee and has wings. Ariel came to the Safe as a teenager, and lived in the city before then, but she is slow to trust others, including Matthew, and runs away frequently.Safe is threatened by an exile, known as Corner, who works with an army of shadows. Eventually, Corner invades Safe by following Ariel home after one of the times she ran away. This causes the community to disperse Above, which is actually downtown Toronto. Once there, with the help of Ariel, Matthew has to reunite his community, and reclaim Safe. In order to do this, Matthew must discover the history of Corner, and its connection to Safe. He learns that there are two sides to every story, and not everything is black and white. Good people can make mistakes, and love and relationships are complex and defining elements of what it means to be human.Above has important messages about themes of “good” and “evil” and the gray areas in between. By blurring the lines between fantasy, magic, and medicine, these themes are easy to bridge into the real world. The focus on outcasts and disabled people gives the book a unique perspective, and the setting takes readers to both well-known and often passed over areas of downtown Toronto.The book suffers from poor setup, however, and slow character development. Leah Bobet uses a stilted writing style, meant to reflect the main character’s education and state of mind. Often this style makes the plotline difficult to follow, and undercuts some of the more intriguing descriptions of Toronto. Readers are also launched into the world without explanation, which can make it difficult to figure out what is going on for the first half of the book. The story can be even more confusing as it is told in patchworks. Outside of Matthew’s main storyline, the narratives of other characters are interwoven into the book, so not all events are chronological.Above has a good premise that will appeal to the right group of young adults, but with the difficult writing level and the lack of setup, some of the target audience might lose interest before finishing the novel. It is worth nothing that some of the content deals with difficult topics, including mental illness, abuse, disability, poverty, gender-identification, people of different and mixed ethnicities, experimentation on people, and death.Recommended with Reservations: 2 out of 4 starsReviewer: Colette LeungColette Leung is a graduate student at the University of Alberta, working in the fields of Library and Information science and Humanities Computing who loves reading, cats, and tea. Her research interests focus around how digital tools can be used to explore fields such as literature, language, and history in new and innovative ways.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.566 | 0.475 |
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".