Textual determinants of a component of literary identification
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.
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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 | 0.005 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".