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Record W1537366856 · doi:10.20361/g2qp4k

Liar, Liar: The Theory, Practice, and Destructive Properties of Deception by G. Paulsen

2011· article· en· W1537366856 on OpenAlexvenueaboutno aff
Dale Storie

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2011
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsnot available
Fundersnot available
KeywordsLyingDeceptionCharacter (mathematics)DutyFace (sociological concept)PsychologyPsychoanalysisPhilosophySocial psychologyTheologyLinguisticsMathematicsMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.579
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.283
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes2
Has abstractyes

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