Bibliographic record
Abstract
MacGregor, Roy. The Highest Number in the World. Illus. G. Despres. Toronto: Tundra Books, 2014. Print.It shouldn’t surprise you that Roy MacGregor writes a good children’s book about hockey especially if you read the Globe and Mail where he is a sports writer. Admittedly, I’m not a big fan of professional sport with little interest in stats, trades, and game results. However, Roy MacGregor always finds an interesting twist to set his stories apart.So it is with this book. On first glance, it seems to be about a hockey-prodigy; a 9-year-old girl idolizing a famous Canadian female hockey player so much she would give up playing because she has to wear #9 on her new team (not the #22 of her idol). How predictable and boring is that? BUT… her grandma sets her straight on why #9 is actually a better number to live “up” to (incidentally, “highest” in the title refers to height).The illustrations are fantastic - filled with witty references to the life of a Canadian, 9-year-old, hockey-loving girl such as drawings hung with hockey tape, embarrassing Velcro skates with toe-picks, sock-monkeys, and many more.A small quibble is the passive voice used in the first few pages; while chronologically correct, it detracts from the opening action just a little. Then again, hockey games themselves usually build up in intensity and excitement. The main reason for loving this book is the use of history to change perceptions. In a world so concerned with the desires of now, this book reminds us that the present is intimately shaped by the past (even if we don’t quite yet know how). Highly recommended: 4 stars out of 4Reviewer: 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.078 | 0.086 |
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".