Bibliographic record
Abstract
This essay examines the phenomenon of crossover fiction, that is, fiction that crosses from child to adult or adult to child audiences. Crossover literature may be addressed to a mixed-age audience by the author and / or publisher, or it may initially be written and / or published for a particular audience and subsequently appropriated by another in a process of 'cross-reading.' Various types of crossover fiction are examined : adult-to-child crossover fiction, rewritings for a different audience and child-to-adult crossover fiction. In addition, this article looks at the significant role that publishers and marketing strategies play in what is largely a marketing phenomenon. Crossover fiction has been seen by some as an essentially European phenomenon, but it is in fact an important, widespread and expanding global trend, as demonstrated in the author's book Crossover fiction: global and historical perspectives (2009). This essay uses examples from a Canadian corpus to explore the global phenomenon of crossover fiction.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.038 | 0.019 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".