Bibliographic record
Abstract
Asnong, Jocey. West Coast ABCs. Rocky Mountain Books, 2018. Jocey Asnong returns with a new ABC book similar to her Rocky Mountain ABCs but this time highlights the beautiful west coast of Canada. Each page of this board book features one or two letters from the alphabet, a west coast place starting with one of those letters, a full colour illustration relating to that place, and a short phrase using words beginning with the alphabet letter(s) of the page. The target audience for this work would include toddlers, pre-K, and early primary students. Some of the words might be difficult for younger readers so this is a book best read with an adult though even the youngest readers will love the bright colours of the illustrations. Since most of the letters have more than one word, this book better represents some of the different sounds that each letter can make, for example the sounds in paddle versus porpoises which makes this book particularly attractive for anyone working on phonetics with a reader. I would especially recommend this for anyone on the west coast as having familiar places featured in the book would only increase the enjoyment already provided by the wonderful art showcased in this work. Recommended: 3 stars out of 4 Lorisia MacLeod Lorisia MacLeod is an Instruction Librarian at NorQuest College Library and a proud member of the James Smith Cree Nation. When not working on indigenization or diversity in librarianship, Lorisia enjoys reading almost any variation of Sherlock Holmes, comics, or travelling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.403 | 0.300 |
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".