Empathy and Resonance in Interactive Digital Climate Fiction
Bibliographic record
Abstract
Readers of fiction often relate their own experiences to a story’s character or events. Resonance in Fiction is fostered through the power of the narrative to evoke emotions and a sense of reflection in the audience. The extent to which a fictional tale resonates with a reader depends on a number of complex interrelated factors, including, in the case of a story, the structural and stylistic elements of the narrative, and in the case of a reader the subjective experience and the psychological predispositions of a reader, within a larger cultural frame, that encompasses the story and the reader. The core of resonance lies in the emotional impact of fiction. Fictional narratives do not merely convey passive information. They involve the readers at an emotional level leading to the experience of a wide range of feelings. Reading a fiction, therefore, is not a process of simple passive reception; rather the reader actively involves in the process by actively engaging, interpreting and connecting with the narrative. Empathy, the ability to understand and share another's feelings, is essential for building emotional connections in fiction. The readers, therefore, engage in mental simulation, resonating themselves with the character and feeling their emotions and motivations. Theory of Mind in literature refers to the ability of readers to attribute and understand mental states like thoughts, beliefs, intentions, emotions, and desires to characters within a narrative. Readers often involve and connect with characters in stories by interpreting textual cues about the character's thoughts and emotions and the ability to employ Theory of Mind. This process often increases the empathy of the readers. This paper explores the role Theory of Mind plays in establishing resonance in Interactive digital climate fiction, by studying how linguistic cues and images in an Interactive Fiction evoke readers to infer the mental states of characters enhancing emotional engagement and empathy. The study employs a mixed-method approach, using Theory of Narrative Empathy as the theoretical framework. The study includes a pre-test and post-test assessment adapted from the Interpersonal Reactivity Index to measure changes in empathy levels and open-ended questionnaires elicit participants' reflections on resonant moments, before and after engaging with the interactive fiction “The Bitter Sea”. This study will combine both quantitative and qualitative methods, using paired statistical tests to assess changes in empathy scores and thematic analysis to identify moments of emotional resonance linked to Theory of Mind inferences about characters' thoughts and emotions. The results are then cross-referenced with Interpersonal Reactivity Index scores to determine whether engaging Theory of Mind strengthens emotional connections with characters and their stories. The hypothesis of the study is that readers will show a significant increase in empathy levels after engaging with the story, as measured by the Interpersonal Reactivity Index. This research examines how interactive digital fiction can enhance empathy and emotional engagement, offering contributions to narrative studies and digital humanities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".