Textual determinants of a component of literary identification
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
Three experiments were conducted on how properties of the text control one aspect of the process of identifying with the central character in a story. In particular, we were concerned with textual determinants of character transparency, that is, the extent to which the character’s actions and attitudes are clear and understandable. In Experiment 1, we hypothesized that the narrator in first-person narratives is transparent because narratorial implicatures (analogous to Grice’s (1975) notion of conversational implicatures) lead readers to attribute their own knowledge and experience to the narrator. Consistent with our predictions, the results indicated that stating the inferred information explicitly leads readers to rate the narrator’s thoughts and actions as more difficult to understand. In Experiment 2, we assessed whether this effect could be explained by differences in style between the original and modified versions of the text. The results demonstrated that there was no effect of adding text when the material was unrelated to narratorial implicatures. In Experiment 3, we hypothesized that transparency of the central character in a third-person narrative can be produced when the consistent use of free-indirect speech produces a close association between the narrator and the character; in this case, readers may attribute knowledge and experience to the character as well as the narrator. As predicted, the central character’s thoughts and actions were rated as more difficult to understand when the markers for free-indirect speech were removed. We argue that transparency may be produced through the use of what are essential conversational processes invoked in service of understanding the narrator as a conversational participant.
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 | 0.001 | 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