Bibliographic record
Abstract
Mok, Carmen. Ride the Big Machines Across Canada. Toronto: Harper Collins, 2014.“I’m going on a trip from sea to sea. How will I do it? Come ride with me”So begins Ride the Big Machines Across Canada by Carmen Mok, the story of a young boy’s trip across Canada where he imagines driving all of the heavy equipment that he sees along the way. Each double page features the boy in a different province or territory riding a geographically appropriate “big machine” such as a giant dumptruck in Alberta’s oil sands, a streetcar in Toronto, or the ferry to Newfoundland. There are also distinctive provincial/territorial landscapes or cityscapes in the background and Ms Mok has incorporated the provincial flag into each picture as well. The illustrations are colourful and very attractive and the text (just one short sentence on most pages) is rhyming and fun to read out loud.Although the recommended ages for the book are 3-7, the sturdy board book format means that even younger children are also likely to enjoy it. Indeed, my heavy equipment obsessed eighteen month old wanted to view this book over and over again. Young children will enjoy the illustrations and searching for the family motor home in each picture. Parents will enjoy teaching their children about Canada as they read the book together.Recommended: 4 stars out of 4Reviewer: Liz DennettLiz Dennett is a Health Sciences Librarian at the University of Alberta, with a lifelong passion for great books and early childhood literacy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.152 | 0.081 |
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".