Bibliographic record
Abstract
Trochatos, Litsa. Don't. Illus. Virginia Johnson. Toronto: Groundwood Books, 2014. Print.“Don’t start a food fight with an octopus, it has six more arms than you do” is how Don’t begins its advice of the many things you should not do with particular animals and why. This colourful storybook warns of the potential concequences of engaging in a game of badminton with a frog or playing fetch with a turtle.Don’t is a quick and funny read. It is most suitable for children in preschool or kindergarten but it also works nicely with those in grades 1-2 who are learning to read. Virginia Johnston’s watercolour images are the highlight of this book, punctuating the humour and carrying the story along. The heavy cardboard pages also make it suitable for younger children who will enjoy the images of animals doing various activities. The book could have been a bit longer, my co-reviewers (two young nieces) wanted “more don’ts”. Overall, a very enjoyable read.Recommended: 3 out of 4 starsReviewer: Hanne PearceHanne Pearce has worked at the University of Alberta Libraries in various support staff positions since 2004 and is currently a Public Service Librarian at the HT Coutts Education and Physical Education Library. Aside from being an avid reader she has continuing interests in writing, photography, graphic design and knitting.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.543 | 0.498 |
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".