Bibliographic record
Abstract
After plot, the most intuitively important aspect of a story concerns the characters. For example, in some simple stories, characters create the plot: The villain creates a problem that the hero must overcome. In some complex, literary narratives, characterization would seem to be an overriding motivation of the implied author, with the events of the narrative merely serving to provide information about the characters. Not surprisingly, then, character and characterization have been a productive area of scholarship in narratology and literary studies. Important work has also been done in linguistics and discourse processing. Our view is that work in personality and social psychology is also directly relevant to understanding character in narrative, although the connection has not always been made in discourse processing research. In this chapter, we review some of the work in these disciplines. We then discuss some categories of features that are relevant to character in narrative and provide a general framework for how these might be used by readers. Finally, we provide some evidence on the use of characterization features by readers. Theories of Literary Character Theories of literary character can be situated on a continuum ranging from traditional to contemporary and more radical models. (For an excellent coverage of these theories, see Margolin, 1989, 1990b). The central issue in this debate has been the relationship between literary character and real people. Traditional theories treated literary characters uncritically as analogues of real people.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".