Mutual Identities: Fostering Empathy between Readers and Characters in Reading a Work of Fiction
Bibliographic record
Abstract
Empathy is often seen fostered through teaching of literature, particularly fiction, and influencing readers’ behavior and emotions. This study aimed to investigate the relationship between readers' and characters' mutual identities as witnessed in the form of a literary empathy created by reading a work of fiction. A qualitative research design helped in in-depth understanding of participants’ experiences, by adopting an empirical stylistic approach to conduct a thematic analysis of readers’ responses, combining stylistic-narratological analysis of two sampled texts, Amy Tan’s Two Kinds and Charles Dickens’ Oliver Twist. Both datasets were collected through a Literary Response Questionnaire (LRQ) and analyzed to understand how literature, particularly fiction, stimulated empathy in individual readers. The readers’ responses mechanism was aptly supported by Theory of mind and Decety's and Gerdes' empathy development theories to understand how readers develop mental flexibility and experience emotional empathy to understand characters’ emotions. The results revealed the intensity of readers’ connection with the characters while they introspected through the text and correlated themselves with human emotions resulting in empathy with the characters of the text. The findings imply that literature equips students and readers with the capacity to cultivate empathy through an emotional appraisal of characters.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".