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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 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