Bibliographic record
Abstract
Parenteau, Shirley. Bears in a Band. Candlewick Press, 2016.This is a great little book. Children will love the rhymes, which tell the story of four brightly coloured teddy bears, who pick up instruments and begin to play. They make a joyful noise that eventually wakes “Big Brown Bear." Instead of being angry, Big Bear joins as a conductor and the music becomes even better.There are two music messages in this book. First, parents should celebrate their children’s musical activities and accept that there will be noise. Second, everyone should attempt to find the music in themselves, and share that with everyone.The text is simple. Young children will quickly memorize it. “The bears all play a noisy song/They don’t care if the notes are wrong." The images are happy, uplifting and full of warm fuzzies. This would be a good bedtime picture book for young children. I highly recommend this book for libraries. Highly recommended: 4 stars out of 4 Reviewer: Sean BorleSean Borle is a University of Alberta undergraduate student who is an advocate for child health and safety.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.161 | 0.132 |
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".