Bibliographic record
Abstract
Major, Kevin. Eh? to Zed. Illus. Alan Daniel. Toronto: Red Deer Press. 2000. Print. Alphabetically-themed children’s books are far from a novel concept; however, when aided by inventive illustrations and a cleverly-chosen word selection, these books can be a welcome addition to any child’s library. Unfortunately, this is not the case for Kevin Major and Alan Daniel’s Eh? to Zed. Major attempts to distinguish his book from other Canada-themed alphabets by choosing a selection of obscure or little-known words. However, while words such as Rockies, poutine, and Gretzky should be of little trouble, Ogopogo, potlatch, Tuktoyaktuk, and kittiwake may be baffling to parents of all education levels, let alone their children for whom the book is ostensibly written. Major does provide brief explanations for his word choice at the end of the book, but given some of his selections, a pronunciation guide would also have been very helpful. Alan Daniel’s illustrations are unoriginal at best, but more often serve to obfuscate the already frustrating text. Each page highlights four words at the top with the illustrations jumbled together below. Considering the relative obscurity of many of Major’s word choices, Daniel’s overlapping illustrations make it even more difficult for the reader to match the word with its corresponding picture. The one proper use I can think of for this book would be for Canadian immigrants who are interested both in learning to read English and researching different facets of Canadian history. While matching words to pictures would still be a mind-numbing task, the words themselves and Major’s explanations might serve as a portal to learning more about Canadian culture. Additionally, Eh? to Zed makes a noticeable effort to highlight Canada’s multicultural nature, incorporating many aspects of French and Native culture. However, at times the book’s efforts at political correctness verge on the ridiculous, such as when the illustrated Mountie is depicted wearing a turban rather than traditional Stetson. While I may hesitantly recommend this book to adult immigrants, I would keep it far away from children. Not Recommended: 1 out of 4 stars Reviewer: Amy PatersonAmy Paterson is a Public Services Librarian at the University of Alberta’s H. T. Coutts Education Library. She was previously the Editor of the Dalhousie Journal of Interdisciplinary Management and is very happy to be involved in the Deakin Review and the delightful world of children’s literature.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.332 | 0.333 |
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".