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Record W2341177698 · doi:10.20361/g24s4b

Wild Eggs: a tale of Arctic Egg Collecting by S. Napayok – Short

2016· article· en· W2341177698 on OpenAlexvenueaboutno aff
Sandy Campbell

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsWrightStyle (visual arts)ArcticThe arcticWhite (mutation)GirlHistoryArt historyArchaeologyGeographyArtVisual artsEcologyPsychologyBiologyOceanography

Abstract

fetched live from OpenAlex

Napayok – Short, Suzie. Wild Eggs: A Tale of Arctic Egg Collecting. Illus. Jonathan Wright. Iqaluit: Inhabit Media, Inc., 2015. Print.Wild duck eggs are a traditional food for Inuit people. This book is about a little girl, Akuluk, from Yellowknife who visits her grandparent in Nunavut and goes with them to gather duck eggs. This is a modern story that is told factually. Akuluk arrives in an aircraft, is picked up in a taxi van and her grandfather uses an all-terrain vehicle to go out onto the land. It is also a story that teaches traditional ways. Inuit words, such as munniit (eggs) and palaugaaq (bannock) are explained and appear in a pronunciation guide at the end of the book. The traditional ways, such as never taking nests that have more than four eggs in them, are explained as Akuluk’s grandfather teaches her.The text is overprinted on Jonathan Wright’s artwork. Parts of his pictures are quite clear and detailed, while others are suggestive and indistinct. This style works particularly well for the “almost invisible” caribou, “his brown and white fur match[ing] the rocks around him”.Wild Eggs is a clearly-written work that incorporates Inuit traditional knowledge with ease. The book is also available in Inuktitut and is recommended for school and public libraries and particularly for libraries that collect polar children’s literatureHighly Recommended: 4 stars out of 4Reviewer: Sandy CampbellSandy is a Health Sciences Librarian at the University of Alberta, who has written hundreds of book reviews across many disciplines. Sandy thinks that sharing books with children is one of the greatest gifts anyone can give.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.060
Threshold uncertainty score0.201

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 studies0.0020.001
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0600.045

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 head0.014
GPT teacher head0.348
Teacher spread0.333 · 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.

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

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
Published2016
Admission routes2
Has abstractyes

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