Bibliographic record
Abstract
Morstad, Julie. how to. Simply Read books, 2013. Print. Anyone who is a fan of creative and lateral thinking will love this book. The simple text and illustrations evoke complex connections and imagination. The title gives away that it is a “how to…” book but the things to do and learn are not your usual “… make cookies” or “… build a birdhouse.” I love that the text problems are answered by text-less illustrations. For example, “how to make new friends” is answered by an image of a child making sidewalk chalk drawings of various creatures (including people) and “how to wash your socks” is accompanied by a group of children stomping in a puddle of clean-looking water. While a few “how to’s” are answered with several possibilities, most have only one. This might be considered a weakness or, on further reflection, the multiple-answer examples suggest a pattern so the reader will search for their own variations.I’ll admit to some discomfort with the choice to make all the “how to” phrases unpunctuated and in lower case letters because I believe proper writing is learned through example. However, it is a tiny quibble about an inspirational book. I will be sure to feel the breeze and appreciate the face wash on my bike ride home in the rain.Recommended: 4 out of 4 starsReviewer: David SulzDavid is a Public Services Librarian at University of Alberta and liaison librarian to Economics, Religious Studies, and Social Work. He has university studies in Library Studies, History, Elementary Education, Japanese, and Economics; he formerly taught in schools and museums. His interests include physical activity, music, home improvements, and above all, things Japanese.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.434 | 0.475 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".