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Record W2281991104 · doi:10.20361/g21593

Noodle & Lou by L. G. Scanlon

2011· article· en· W2281991104 on OpenAlexvenueno aff
Tami Oliphant

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsFriendshipFeelingPsychologyArtArt historyVisual artsPsychoanalysisSocial psychology

Abstract

fetched live from OpenAlex

Scanlon, Liz G. Noodle & Lou. Illus. Arthur Howard. New York: Beach Lane Books, 2011. Print. In an unlikely animal pairing, Noodle, an earthworm who is having a bad day, turns to his buddy Lou, a perky blue jay, to cheer him up. While Noodle confides his feelings of envy about other worms living it up at Wiggly Field and catalogues all of his reasons for self-loathing, Lou counters with loyal support until finally Noodle cheers up. Lou’s patient, nurturing, and kind-hearted reassurance highlights the importance of friendship and self-acceptance. There are moments of entertaining weirdness in the book like when Noodle reasonably complains “My head has no eyes” but at times the text is clichéd and seems forced to fit into the rhyming scheme. The book does not stray from the author’s purposive, straightforward storyline and lessons—developing self-esteem, supporting friends, and appreciating who you are. Kids will pay attention to the thick-lined, colourful drawings and try to spot other bugs and birds found in the illustrations. The illustrations get progressively brighter and more detailed as Noodle’s mood brightens. Noodle is drawn as a ball-cap wearing, charming worm who expresses a surprising range of emotions through body language and his mouth. Noodle & Lou is an old-fashioned story about the importance of friendship. It is aimed at children ages 4-6. Recommended with reservations: 2 out of 4 stars Reviewer: Tami Oliphant

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.232
Teacher spread0.215 · 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.

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 routes1
Has abstractyes

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