Processing Components of Perspective Taking
Bibliographic record
Abstract
In Chapter 5, we discuss the processing components that underlie the perspective-taking analogy that we articulated in Chapter 2. This analysis makes it clear that the retrieval of personal knowledge and experience is critical, and we review some of what is known about episodic retrieval and how it can be used in this context. In forming an analogy, one must be able to identify how elements of the story world are related to corresponding elements in one’s own experience. To understand this process, we discuss how readers must construct similarity relations. Finally, we discuss the mechanics of analogy formation per se and describe the notion of a structural mapping between the reader and the character that underlies the perspective-taking analogy. We close out Chapter 5 with a discussion of perspective-taking dynamics. This includes an illustration of how perspective taking can be driven by the events of the story world or evaluations of the character. As we make clear, perspective taking is an ongoing process that can unfold in a variety of ways over the course of reading a narrative.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".