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Record W2905266726 · doi:10.20361/dr29386

Smoot by M. Cuevas

2018· article· en· W2905266726 on OpenAlexvenueno aff
Lorisia MacLeod

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Methods and Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsShadow (psychology)TundraVocabularyArtVisual artsPsychologyArt historyLiteraturePsychoanalysisPhilosophyLinguistics

Abstract

fetched live from OpenAlex

Cuevas, Michelle. Smoot. Illustrated by Sydney Smith. Tundra Books, 2017. 
 Smoot is the story of an adventurous shadow that is attached to a rather unadventurous boy until one day Smoot finds himself detached and free to live out his wildest dreams. Even other shadows are inspired in this short but warm tale on the importance of childish joy in the simple things. Young readers will enjoy the 48 pages of colourful images that accompany the story though they would likely best enjoy the story read to them as some of the vocabulary may be tricky for young readers. The illustrations are similar to some of Tundra Book’s other publications such as If a Horse Had Words and will delight adults in addition to younger readers.
 This would be a lovely recommendation for any young reader who enjoys Peter Pan’s shadow since there are a number of similarities in the shadows’ demeanours. The slightly oversized size of the book makes this an excellent choice for classroom or library storytimes. In fact, this story could easily be used as part of a storytime program where children could be asked what brings them joy or even asked to act it out with their shadows. Overall, I would primarily recommend this book to parents and libraries though elementary teachers may find this work to be beneficial to start discussions about students’ hopes and dreams.
 Recommended: 3 out of 4 starsReviewer: Lorisia MacLeod

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)0.002
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.652
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.380
Teacher spread0.363 · 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
GenreReview

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 routes1
Has abstractyes

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