Bibliographic record
Abstract
Many of the most celebrated children's books have a famous origin story attached to them. Lewis Carroll made up 'the interminable fairy-tale of Alice's Adventures ' (as he called it in his diary) while he was on a boat-trip with Alice, Lorina and Edith Liddell in 1862; Peter Pan grew out of J.M. Barrie's intense friendship with the five Llewelyn Davies boys; Salman Rushdie, following the Ayatollah Khomeini's 1989 fatwa , wrote Haroun and the Sea of Stories for his son, Zafir, for Zafir, like Haroun, had helped his father recover the ability to tell stories. The veracity of these stories, and many others like them, is open to question. But their prevalence and endurance is nevertheless important. We seem to demand such originary myths for our children's classics. What we want, it appears, is the assurance that published children's books have emerged from particular, known circumstances, and, more specifically, from the story told by an individual adult to individual children. C. S. Lewis listed this as one of his 'good ways' of writing for children: 'The printed story grows out of a story told to a particular child with the living voice and perhaps ex tempore .' Such a creative method is an antidote to what Lewis thought the very worst way to write for children, striving to 'find out what they want and give them that, however little you like it yourself'.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.015 | 0.041 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".