Bibliographic record
Abstract
Greetings Everyone! There are only a few news items for the fall issue: International Board on Books for Young People (IBBY) Announces List of 100 Children’s Books in Arabic "Here is a selection of 100 children’s books in Arabic published in various countries of the Arab world. This selection reflects the dynamism of a sector that has truly taken off in the past twenty years, with the publication of a wide range of titles whose quality is often recognised by international awards."Finalists Announced for the 2017 Canadian Children’s Book Centre AwardsThe CCBC has announced the finalists in for their annual book awards. This includes the $30,000 TD Canadian Children’s Literature Award. See the CCBC website for a full list of finalists."So you want to get Published?" SeminarThe Canadian Children's Book Centre is hosting a seminar for aspiring children's book authors on November 4, 2017 at 10:00 AM at the Northern District Library in Toronto, ON. Details are found on the CCBC website.Vancouver Children's Literature Roundtable (VCLR) The VCLR, announced the 2017 Information Book Award Shortlist. The shortlist can be found on the VCLR webiste. The winner will be announced in November 2017.Wishing you bright fall days!Hanne Pearce - Communications Editor
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.692 | 0.683 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".