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Record W2804973259 · doi:10.20361/dr29331

Under the Bed Fred by L. Bailey

2018· article· en· W2804973259 on OpenAlexvenueaboutno aff
Sean Borle

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMonsterComicsStyle (visual arts)Reading (process)TypefaceArt historyArtMedia studiesLiteratureVisual artsSociologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Bailey, Linda. Under the Bed Fred. Tundra Books, 2017.In this offering of the “monsters are not scary” genre, award winning author Linda Bailey has written a chapter book for newly independent readers. There are five chapters telling the story of Leo, who is afraid of the monster, Fred, who lives under his bed. Eventually Leo befriends Fred and discovers he is not scary. He takes Fred to school, where Fred defends him against the class bully, who is portrayed as a red-headed child with a green shirt. Most readers will relate to dealing with a bully at school.The book is well paced for a new reader’s daily reading time. The text is simple and nearly every page has an illustration. One can imagine a child in Grade 2 or 3 being able to read a chapter each day and feel success at having completed a 63 page book by the end of a week.The illustrations are comic style. The monster looks a lot like a brown bear. There are lots of action images, extreme expressions, and speech balloons. The text appears as a very large typeface to emphasize something scary or loud. Sometimes the text is printed at an angle and sometimes words like “KNOCK! KNOCK! KNOCK!”, “GRRRRRROWWLL!” and “CRASH! OOF! POP!” are printed over images for effect.Overall, this is a good book and it is therefore recommended for public and school libraries. Recommendation: 3 stars out of 4Reviewer: Sean C. BorleSean Borle is a University of Alberta undergraduate student who is an advocate for child health and safety.

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 score0.898
Threshold uncertainty score0.998

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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
Published2018
Admission routes2
Has abstractyes

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