Bibliographic record
Abstract
Winters, Kari-Lynn. On My Bike. Illustrated by Christina Leist. Tradewind Books, 2017. Following their award-winning 2009 book, On My Bike, Winters and Leist have created a delightful, easy to read story following a young child taking a bicycle ride with their parents. The book, clearly designed to be read aloud, establishes a simple rhyming pattern which allows both narrative and sound effects to connect with the reading experience. Much of the enjoyment of the story comes from the connected sound effects and the structure, wherein the protagonist, cleverly left both unnamed and without a defined gender, goes on a bicycle ride with one parent while the other stays behind with a younger child. The story follows the two on their bicycle ride and once they have made the end of their trip, follows them back through all of the previous story elements, allowing easier understanding and recognition for younger readers, and ending up back with both parents. A very simple story following a relatable event for young cyclists, as well as those getting ready to begin cycling, On My Bike has a warm and welcoming style that would work great for preschoolers aged three to five. Highly recommended: 4 out of 4 starsReviewer: Kirk MacLeod Kirk is the Open Data Team Lead for the Government of Alberta’s Open Government Portal. A Life-Long reader, he moderates two book clubs and is constantly on the lookout for new great books!
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.498 | 0.371 |
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".