Bibliographic record
Abstract
Postcards Roxanne Harde Award winning author Pat Mora teamed up with her daughter Libby Martinez and illustrator Amelia Lau Carling to produce this lovely picture book that will delight all ages of readers. Based on a bilingual joke from Mexican American folklore, this is the story of Chico Canta, the youngest in a family of twelve mice, who live with their parents and community in a theatre. The mice pride themselves on their multilingualism, speaking English, Spanish, Italian, Moth, Cricket, and Firefly. Young Chico learns to speak Dog and saves their play and the day when a cat interrupts their new production. Mora and Martinez tell the story with charm and ease; Carling’s illustrations are bright and appealing, with just enough detail to captivate young audiences. The story makes bilingualism equally appealing, offering the message of language education with subtlety and humor. This is a truly wonderful new picturebook. Pat Mora & Libby Martinez Bravo, Chico Canta! Bravo! Illustrated by Amelia Lau Carling Toronto: Groundwood, 2014 Unp. ISBN: 9781665093438 (Picturebook; ages 3+) [End Page 9] Copyright © 2015 Bookbird, Inc.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.884 | 0.804 |
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".