Bibliographic record
Abstract
Perhaps the most salient and obvious characteristic of a narrative is its plot, that is, the temporal, causal, or logical sequence of events that provide the basis of the story: Most, when asked to describe a narrative, will respond with a summary of the plot. Attempts at characterizing the nature of events and plot go back at least as far as Aristotle, and it has been a central problem in the scholarship on narratology since the pioneering work of Propp (1968; originally published in 1928) on story grammars. Yet more than seventy years after the original Russian publication, our conceptual understanding of narrative events and plot is far from complete. In this chapter, we summarize some of the issues in the debates concerning plot structure. Our analysis is that at the heart of many of the controversies lies the issue of how the reader's experience of plot should be handled and, in particular, the problem of distinguishing textual features from reader constructions. There is a surprising similarity among the debates that have gone on in literary studies, discourse processing, and artificial intelligence concerning the appropriate starting point in the analysis of plot. In all these domains, an important question has been whether it is more appropriate to analyze plot structure based on the manner in which the story is told, that is, as a property of the discourse, or as reflecting properties of the story world itself.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".