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Record W2889361274 · doi:10.20361/dr29372

Don't Wake Up Tiger by Britta Teckentrup

2018· article· en· W2889361274 on OpenAlexvenueaboutno aff
Alexandra Adams

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSurpriseVisual artsNarrativeStyle (visual arts)TigerStorytellingArtEvocationLyricsFableLiteratureHistoryCommunicationPsychologyComputer science

Abstract

fetched live from OpenAlex

Nosy Crow Limited. Don’t Wake Up Tiger, 2018. Version unlisted. Apple App Store, https://itunes.apple.com/ca/app/dont-wake-up-tiger/id1336103707?mt=8 Suggested Age Range: Preschool (ages 3-5)Cost: $1.39 Don’t Wake Up Tiger follows the aesthetically inviting, kinesthetically engaging style and storytelling present in Britta Teckentrup’s 2016 picture book, Don’t Wake Up the Tiger. This iOS app includes an oral retelling (narrated by Charlotte Rose Allen) with kinesthetic prompts, as well as a song clip, and two themed games. A gentle story about animal friends working together for a tiger’s birthday surprise, this multimedia text is a virtually wordless version of the physical book. Embracing the narrative and interactive style common to Teckentrup’s other works, the user can see the effects of their actions animated, such as blowing on a balloon, rubbing Tiger’s nose and rocking him to sleep, rather than relying on illustrations in the book to mimic such movements through page turns. There are instances in which written text might have been more purposefully integrated for this audience, for example, including highlighted lyrics to the familiar Happy Birthday song or inserting key words during the story, such as “pop” when a balloon bursts. Prompts given during the story are only offered once orally by the narrator (accompanied by a vague visual aid), and are thereafter primarily text-based, possibly necessitating a supervising adult to intervene with additional prompting, particularly during the first play. Although these details do not detract from the overall quality of the app, such minutiae may deter first time users if they cannot complete the actions and play/listen/interact with the story intuitively on their own. Both games offered are themed with the characters and colour palette from the story, and are of varying levels of difficulty; Matching Pairs is a traditional flip and pair memory activity, while Spot the Difference is a side-by-side attention to detail task, comparing two images at a time and touching on the item(s) that are different. Simple and charming, Don’t Wake Up Tiger is a lovely, low-key reinterpretation of the physical book, and would make a nice addition to any preschooler’s app selection. Recommended for public libraries and early childhood settings, this app is best suited for children aged three to five. Recommended: 3 out of 4 stars Reviewed by: Alexandra Adams Alex is a busy mom and elementary school teacher, with a passion for early childhood education and the arts. She is currently working on her MLIS at the University of Alberta.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.682
Threshold uncertainty score0.454

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.6820.576

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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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