Bibliographic record
Abstract
McCarney, Rosemary. The Way to School. Toronto: Second Story Press, 2015. PrintThe book is able to show that different communities have very distinct experiences when going to school and a number of them not too pleasant. For example, children are shown wading through a river, climbing ladders up a river bank, riding a zip-line and crossing a single-wire bridge. Despite all these differences the book shows how much children love being in school.With clear and high quality photos, the book is able to demonstrate the different experiences. The photos have captured different sceneries and landscapes that ably demonstrate the different worlds the children come from. The photos add a lot of wonderful content to the story. As the photos demonstrate different communities it is therefore easy for children from various communities to relate with the story as well as appreciate the differences.This book shows both boys and girls going to school. Education of girls is one of author Rosemary McCarney’s goals. McCarney leads Plan International Canada and helped found the “Because I am a Girl” initiative. This is book is recommended for public libraries and children’s rooms everywhere.Highly recommended: 4 stars out of 4Reviewer: Nasra GathoniNasra is a Health Sciences Librarian at the Aga Khan University in Nairobi, Kenya. Nasra is an avid book reader, a passion she nurtured from her childhood and believes in the necessity of children reading from a tender age.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.094 | 0.067 |
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".