Bibliographic record
Abstract
Joshua Landy says “formative fictions” help us fine-tune our mental capacities. This article looks at how novels for young adults may challenge readers to fine-tune their capacities as readers of more complex fiction. Three sample titles ( I Capture the Castle by Dodie Smith, The Tricksters by Margaret Mahy, and Slay by Brittney Morris) make use of character-authors to invite readers to negotiate the terms of reading. Young readers normally have extensive childhood experience in the social negotiation of the terms of make-believe games (“You be the daddy”) and can apply this expertise to the challenge of these novels as they interact with the explicit observations of the heroines about the making of stories. This article takes up Aidan Chambers’ challenge to analyze materials for youth as a separate literature. By exploring the work of three novels published over a 70-year span, (the titles were published in 1948, 1986, and 2019), it meets his demand to include the history of youth literature in our considerations. In these sample texts, young readers are invited to turn back to early childhood in order to make use of the skills and experience of fictional engagement as first developed in pretend games; as a consequence, they develop more subtle capacities as interpreters of complex fiction, thus addressing a major challenge of what Chambers calls “the age between.”
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".