Aesthetic Responses to the Characters, Plots, Worlds, and Style of Stories
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This chapter reviews empirical research on aesthetic responses to stories, organizing our review around characters, plots, worlds or setting, and stylistic choices. We begin by outlining various responses to characters and how they influence us. Next, we discuss emotional, cognitive, and physiological reactions to plot events. We also touch on the confusing appeal of stories that elicit negative emotions, suggesting that they inspire insight. Next, we focus on the worlds in which stories take place, outlining how engagement in story worlds affects enjoyment and story-related beliefs. We also review our tendencies to revisit narrative worlds, and how different worlds map onto different genres. Finally, we discuss how characters, plots, and settings can be portrayed in different ways, based on stylistic choices. We explain how adopting a unique style of presenting stories captures attention and invites reflection and engagement. Lastly, we discuss future challenges and goals facing this field.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it