Liar, Liar: The Theory, Practice, and Destructive Properties of Deception by G. Paulsen
Bibliographic record
Abstract
Paulsen, Gary. Liar, Liar: The Theory, Practice, and Destructive Properties of Deception. New York: Wendy Lamb Books, 2011. Print. Kevin Spencer, the fourteen year-old protagonist of Gary Paulsen’s latest novel, is a consummate and unashamed liar. He considers lying to be second nature, “a way of life”; even going so far to proclaim that it is his duty to lie for the greater good because it makes things easier for everyone. In his words, the secret to successful lying is that “people only listen for what they want to hear, so I only tell them that.” Possessing this particular skill also means he’s never been caught in a lie. This novel chronicles a week in Kevin’s life, the week in which his lying gets out of control and his life goes from “zero to crap”. He lies to his classmate about a medical illness to get out of working on an assignment, lies to his teachers so he can get skip class and spend more time trying to impress a girl, and lies to his parents to get his siblings in trouble. Along the way, he explains the rules behind “good” lying as he rationalizes and justifies his actions, until finally he is forced to face the fact that his lying has consequences. Liar, Liar is full of Paulsen touchstones, including a bright, self-aware teenage protagonist, a cast of quirky supporting characters, and witty and fast-paced dialogue. Ultimately, the novel is stronger on character development and dialogue than on plot. Although the story moves along briskly enough to keep most readers interested, it is not as satisfying or cohesive as those in Paulsen’s other novels. The series of events that precipitate Kevin’s eventual realization about the negative effects of his lying are less dramatic than expected, resulting in a rather understated story overall. Similarly, the romantic interest that drove many of his lies was also left resolved (likely to be continued in the next novel in the series). Nevertheless, anyone who is a fan of Paulsen’s later novels such as Lawn Boy will still find Liar Liar to be an enjoyable read. Even if it’s not always clear where Kevin is going with his consistent lying, his inner monologue keeps the journey entertaining. Recommended: 3 out of 4 stars Reviewer: Dale StorieDale is Public Services Librarian at the John W. Scott Health Sciences Library at the University of Alberta. He has a BA in English, and has also worked in a public library as a children's programming coordinator, where he was involved with story times, puppet shows, and book talks.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".