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Record W4407996971 · doi:10.3366/ircl.2025.0600

The Cuteness Quotient: Penguins, Picturebooks, and Environmental Education

2025· article· en· W4407996971 on OpenAlexaff
Sharon Smulders

Bibliographic record

VenueInternational Research in Children s Literature · 2025
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsMount Royal University
Fundersnot available
KeywordsQuotientMathematicsPure mathematics

Abstract

fetched live from OpenAlex

This article explores how the aesthetic of cuteness shapes representations of nature in six informational picturebooks about penguins. The analysis addresses the two informational genres – one related to the emperor life cycle and the other to penguin rescue – that dominate the field in the twenty-first century. Whereas the books devoted to the emperor life cycle tend to reject cuteness as a mode inconsistent with scientific observation, those devoted to penguin rescue show its allure as a means, albeit problematic, to stimulate interest and raise awareness of threats to species survival. Within this context, cuteness often obscures the ideological, political, and economic forces driving eco-disaster. By contrast, picturebooks depicting charismatic animals show the value of awe and wonder in teaching respect for the natural environment. Emperor life-cycle narratives examined include Martin Jenkins and Jane Chapman’s The Emperor’s Egg; Sandra Markle and Alan Marks’s A Mother’s Journey; and Nicola Davies and Catherine Rayner’s Emperor of the Ice. Penguin rescue narratives examined include Sophie Cunningham and Anil Tortop’s Flipper and Finnegan: The True Story of How Tiny Jumpers Saved Little Penguins; Marikka Tamura and Daniel Rieley’s Penguins Don’t Wear Sweaters; and Jean Marzollo and Laura Regan’s Pierre the Penguin: A True Story.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.274

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 studies0.0000.000
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 head0.020
GPT teacher head0.388
Teacher spread0.368 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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