Bibliographic record
Abstract
Recent studies in cognitive literary criticism have provided scholars of literature with new, stimulating approaches to literary texts and neuroscientists with new insights about human emotions, empathy, and memory through evidence from fiction. What have so far been largely neglected are the implications of cognitive criticism for the study of literature targeting a young audience, whose theory of mind and empathic skills are not yet fully developed. A cognitive approach to children's and young adult literature has to meet several challenges less relevant in general fiction. Firstly, how is a young fictional character's consciousness represented by an author whose cognitive and affective skills are ostensibly superior? Secondly, how do texts instruct their young readers to employ theory of mind in order to assess both the young protagonist's emotions and their understanding of other characters' emotions (higher-order mind-reading)? Thirdly, how can fiction support young people's development of their theory of mind? The paper will discuss these issues with a particular focus on memory and identity, expressed textually through tense and narrative perspective. Drawing on work by Lisa Zunshine (2006) and Blackey Vermeule (2010), the predominantly theoretical argument will be illustrated by a contemporary young adult novel, Slated (2012), by Teri Terry.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".