Putting Perspective Taking in Perspective
Bibliographic record
Abstract
We present a new framework for the discussion of perspective taking, particularly with reference to the processing of literary narrative. In this framework, adopting a perspective entails matching evaluations with those of the narrative character. This approach predicts that perspectives should be piecemeal rather than holistic, dynamic rather than consistent, effortful rather than automatic, and reactive, in the sense that they are a function of the reader's online processing as it interacts with narrative technique. We describe evidence from an interpolated evaluation method in which readers are periodically interrupted and asked to rate evaluations from a character's perspective. The results indicate that interpolated evaluations interact with narratorial stance to determine a character's transparency—that is, the extent to which she is rational and understandable. In particular, interpolated questions increase transparency of the focal character when there is minimal narratorial guidance, but decrease transparency when the narrator adopts a relatively distanced stance towards that character. These results demonstrate that perspective taking depends on the details of a reader's processing over the course of the story.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".