Bibliographic record
Abstract
Pizzoli, Greg. Good Night Owl. Scholastic Inc., 2016.Good Night Owl, an illustrated picture book, is an easy read that will keep younger children looking for the mouse hiding in the pictures, and wondering what Owl will do next. The predictable pattern of Owl hearing a noise and then reacting to it continues throughout the story. These reactions quickly grow in severity, with Owl checking to see if it was the wind at the start, to tearing down the walls of the house with a sledge hammer at the end. When there are no more options for extreme reactions, Owl realizes that the noise wasn’t so bad after all and ends up going to sleep peacefully. The story begins with a calm and quiet tone, continues with progressive excitement, and finally ends with the same calmness of the beginning, lulling Owl to sleep. The cartoon-like illustrations enhance the story and text by giving the reader a visual representation of the emotions that Owl is experiencing. Children will easily be able to understand how Owl feels at each point of the story.Owl’s reactionary behaviour could be a good springboard in discussion to show the importance of thinking before acting. Though the consequences for Owl’s reactions are not mentioned (only briefly shown in the pictures as his house is destroyed), my children easily saw how these actions were not necessary, thought of what could have been done instead, and together we laughed a lot along the way. This is a wonderfully fun and easy read that would be a welcome addition to an elementary classroom or library.Recommended: 3 out of 4 starsReviewer: Lisa SeilerLisa Seiler is a grade 4 teacher with Edmonton Public Schools and mother of two girls, aged 6 and 10 years.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.278 | 0.313 |
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".