Bibliographic record
Abstract
Isop, Laurie. Illustrated by Gwen Millward. How do you Hug a Porcupine? New York: Simon & Schuster Books for Your Readers, 2011. Print. A charming and hopefully harmless little book. The variety of animals is illustrated realistically enough to be recognizable on the page and probably even in real-life. The text is nicely lyrical with a rhythm and rhyme that makes you want to sing while reading. The message, however, is not all positive. On the good side, the portrayal of animals as worthy of human attention, respect, and appreciation (although not particularly original in children’s books) is always welcome. Also nice is the mix of familiar animals (e.g. cow, horse, pig, giraffe) with some that don’t get much attention (e.g. hedgehog, yak, ostrich) so young readers might learn something new. On the negative side, however, is the encouragement to hug any-and-all animals. Knowledge of the difference between tame (domestic) animals and wild animals should be instilled from a young age and, even if not taught explicitly, children’s authors should at least not introduce ideas that must be unlearned in real life. Pandas, yaks, porcupines, kangaroos, and dolphins should NOT, as a general rule, ever be hugged and people should NOT be convinced that everything needs a hug. For one thing, animals are unpredictable and potentially dangerous to the hugger. For another, hugging or touching a wild animal can be dangerous for the hugged - hugging a porcupine would dislodge many quills and reduce its defenses against predators. Sometimes I wonder if the national park tourists who slather honey on their child’s arm to get a picture of the cute bear licking it or approach a fully-grown elk to touch its antler velvet were maybe too exposed to this sort of book. In short, the answer to “how do you hug a porcupine?” should be, “you don’t!” Stick to hugging your own kitty-cat or puppy-dog instead that you know will probably appreciate it and not attack you. Recommended with reservations: 2 out of 4 stars (charming and lyrical but potentially dangerous in later life). Reviewer: David Sulz David is a librarian at the University of Alberta working mostly with scholars in Economics, Religious Studies, and Social Work. His university studies included: Library Studies, History, Elementary Education, Japanese, and Economics. On the education front, he taught various grades and subjects for several years in schools as well as museums. His interest in Japan and things Japanese stands above his other diverse interests.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.160 | 0.168 |
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".