Bibliographic record
Abstract
Reviewed by: Little Red Lies by Julie Johnston Elizabeth Bush Johnston, Julie Little Red Lies. Tundra, 2013 340p ISBN 978-1-77049-313-1 $19.95 E-book ISBN 978-1-77049-314-8 $10.99 R Gr. 7-9 Adolescence isn’t sitting well with Rachel, whose drama-queen propensities amplify her reaction to every perceived trouble or slight—and life isn’t particularly trouble-free in her family at the moment. Her beloved brother Jamie is just home from World War II, recovering well from his physical injuries, but he’s jobless, at odds with his girlfriend, and now diagnosed with leukemia. While the family turns the lion’s share of attention to Jamie, Rachel tries to establish herself in the school drama department, where she proves to be ineffectual as an actress. She does, however, catch the attention of the young teacher/director, Mr. Tomkins, who seems to know just which girls are in distress and preys on their vulnerabilities. Although this Canadian import is a work of historical fiction, readers who favor domestic drama will be the audience most appreciative of Rachel’s social missteps. Driven by her own over-emotional nature and casual attitude toward deceit, Rachel is often as shameless as the Little Red Lies crimson lipstick she sports to make herself appear sophisticated. Her mistakes have a way of making her sympathetic, though, and by the end, she’s a bit more self aware; her growth—and potential for further wising up—is credibly portrayed. Secondary characters are robustly drawn, and the unpredictability of the plotline should keep readers engrossed through each of Rachel’s cringe-worthy blunders. Copyright © 2013 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.054 | 0.029 |
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".