Bibliographic record
Abstract
Jocelyn, Martha and Nell Jocelyn. Where Do You Look? Toronto: Tundra Books, a Division of Random House of Canada, 2013. Print.This picture book is a playful exploration of homonyms—in this case, those that are spelled alike. The text comprises a series of questions; e.g., “Where do you look for a letter? In the mailbox?” Or on the page?” The illustrations, which incorporate collage and photographic techniques, are colourful and well defined—perfect for story hour viewing. The accompanying questions are an invitation to participate; children will enjoy guessing what further meanings of a word might next be illustrated. Beyond story hour, the book is appropriate, both in terms of font size and vocabulary, for independent reading by beginners.A further possibility for this book is its use in English as a Second Language classes. Gleaning the contextual meaning of a word is always difficult when learning a new language and the Jocelyns provide a light-hearted approach to the problem. To avoid the sensitive issue of using a beginner’s book in a lesson for older students and adults, introduce it as something an ESL learner might like to share with a child. (Confess: those of us who love children’s literature have been playing that card forever!)Highly Recommended: 4 out of 4 starsReviewer: Leslie AitkenLeslie Aitken’s long career in librarianship involved selection of children’s literature for school, public, special, and university collections. She is a former Curriculum Librarian at the University of Alberta.
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 | 0.002 |
| 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 | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.234 | 0.247 |
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".