Bibliographic record
Abstract
Reviewed by: Bike Thief by Rita Feutl Elizabeth Bush Feutl, Rita. Bike Thief. Orca, 2014. [136p] (Orca Soundings). ISBN 978-1-4598-0570-5 $16.95 ISBN 978-1-4598-0569-9 $9.95 ISBN 978-1-4598-0572-9 $9.95 Reviewed from galleys R Gr. 7-9. Katie’s innate clumsiness has resulted in some domestic damage, and her brother, narrator Nick, is afraid that their foster parents—one of the rare families willing to take on a pair of siblings—will ask for the siblings’ reassignment. Therefore, Nick makes a quick deal with a shady dealer, Dwayne, to replace a broken TV, on the condition that Nick pays him back in service. The favor, it turns out, is to recruit and direct “runts,” underage kids with no criminal record, to steal high-end bicycles and help rebuild them at the chop shop Dwayne supervises. Naïve and desperate, Nick complies, using his love of fixed-up bikes to spot prime merchandise. It quickly becomes evident, though, that the chop shop is merely a front for a serious drug ring, and when another recruiter like Nick is beaten and kidnapped, Nick must decide whether to expose his own crimes to save the boy’s life. The compressed time frame of the episode fits well into the Orca Soundings hi-lo format, and the plot boasts notable authenticity in taking inspiration from a real-life Edmonton workspace that, according to the acknowledgments, helps kids “build and maintain their own bikes.” Details of the thieves’ m.o. are less likely to serve as instructions in larceny than as a warning to protect your own property from crimes of opportunity. This title will serve not only the hi-lo crowd but any YA reader looking for a lightning-paced super-quick pick. [End Page 516] Copyright © 2014 The Board of Trustees of the University of Illinois
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.142 | 0.095 |
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".