Bibliographic record
Abstract
Young, Cybèle. Ten Birds. Toronto: Kids Can Press, 2011. Print.Faced with the prospect of fording a river, ten chubby, adorable birds use found objects to devise vehicles that will convey them, one by one, to the other side. Sound simple? Perhaps not. When Ten Birds won the 2011 Governor General’s Literary Award for Children’s Illustration, the jury praised it as a “visual riddle.” And indeed, there does seem to be more to this enigmatic counting book than mere plot summary can provide. For starters, the birds’ predicament is innately absurd: why don’t they just fly across the river? Or make use of the bridge that looms in the background of every illustration? And what are we to make of the birds’ report-card epithets (Outstanding, Quite Advanced, Needs Improvement), which may or may not correspond to their river-crossing ingenuity? Perhaps Ten Birds is less about birds and rivers than our tendency to overcomplicate the simplest problems, confusing complexity with elegance. Young’s exquisite pen-and-ink illustrations certainly suggest a tension between the two: dense, furious skeins of crosshatching resolve into minimalistic set pieces, in which vast swaths of negative space dwarf the objects depicted. Come to think of it, perhaps the book is simply an extension of Young’s prevailing sensibilities as a visual artist. Known for her miniature sculptures in Japanese paper, Young has a gift for amplifying the strangeness of everyday objects by reproducing them, out of context and much reduced, in a vacuum of blank space. Likewise, her ten flightless birds and their bizarre contraptions, intricately rendered yet utterly inexplicable, riddle the reader with their presence. (And, like the paper sculptures, they are darn cute.) The fact that Ten Birds refuses to explain itself is one of its principal charms, and its mystery will beguile readers young and old.Highly recommended: 4 out of 4 stars.Reviewer: Sarah Mead-WillisSarah is the Rare Book Cataloguer at the University of Alberta's Bruce Peel Special Collections Library. She holds a BA and an MLIS from the University of Alberta and an MA in English Literature from the University of Victoria.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.241 | 0.184 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".