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Record W49004973 · doi:10.20361/g2tk5v

Big Brothers Don't Take Naps by L. Borden

2013· article· en· W49004973 on OpenAlexvenueaboutno aff
Allison Sivak

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsBrotherSisterReading (process)CredibilityBook designVisual artsArtSociologyPhilosophyLinguisticsEpistemology

Abstract

fetched live from OpenAlex

Borden, Louise. Big Brothers Don't Take Naps. Illus.Emma Dodd. New York: Margaret K. McElderry Books, 2011. Print.A sweet, playful look at what older siblings do for their adoring youngers. The book is large-format, and Dodd often draws the children actual-size, which can make for an immersive feel of being in the page. The drawings are fairly simple ink drawings, which emphasize the emotions of the characters well, and they use much colour. The design also plays with fonts and text layout, making space for the adult reader to be a bit more playful in the reading – for example, the rocket ship countdown. As well as naming all the things big brothers can do, the story hints at a family secret: a new baby sister is coming, so Nicholas can himself now be a big brother. The book is a warm, positive story that children up to Kindergarten age will love.Recommended: 3 out of 4 starsReviewer: Allison SivakAllison Sivak is the Assessment Librarian at the University of Alberta Libraries. She is currently pursuing her PhD in Library and Information Studies and Elementary Education, focusing on how the aesthetics of information design influence young people’s trust in the credibility of information content.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.141
Threshold uncertainty score0.470

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1410.092

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 head0.009
GPT teacher head0.269
Teacher spread0.261 · 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
Published2013
Admission routes2
Has abstractyes

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