Bibliographic record
Abstract
Twohy, Mike. Poindexter Makes a Friend. New York: Simon & Schuster, 2011. Print. Full disclosure: this is a book, reviewed by a librarian, about making a friend at the library. However, the gentle charms of Poindexter Makes a Friend will appeal to most everyone, regardless of the size of one’s soft spot for libraries. Poindexter is an introverted young pig who would rather read a story to his stuffed animals than go out to play in his neighbourhood. His adores his local public library, where he helps to shelve books and push the book cart. Poindexter’s comfortable life of library volunteerism is interrupted when Shelby, a shy turtle, asks for help finding a book about how to make friends. Some children make friends easily, and some do not. Either way, friends are a pressing concern for the pre-school and early elementary crowd, the audience for Poindexter Makes a Friend. The book’s appeals include the friendly, expressive watercolours of author Mike Twohy, a veteran New Yorker cartoonist. However, the book’s most valuable facet is its discussion of how to make friends, which takes place, not pedantically, between Poindexter and Shelby. This discussion includes concrete, age-appropriate parsing of concepts such as “sharing” and “being nice,” and could make a good launching point for a group discussion at a library story hour or at the beginning of a new school year. This is a tale that is simple, but not simplistic. As Poindexter helps Shelby, so will Poindexter Makes a Friend help bashful souls to emerge a little bit from their shells. Recommended: 3 out of 4 starsReviewer: Sarah PolkinghorneSarah is a Public Services Librarian at the University of Alberta. She enjoys all sorts of books.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.176 | 0.136 |
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".