Bibliographic record
Abstract
The title of this chapter is perhaps presumptuous because it suggests that some problems have been solved. In fact, even for issues we dealt with in some depth, such as narratorial implicatures and narrator–character associations, the present work merely scratches the surface. Thus, it is perhaps more appropriate to regard this work as an outline of an approach or framework, and the presented research provides only an illustration of the kind of work that can be done within that framework. Further, although we have attempted to cover a broad class of issues in the processing of narrative, there are many areas on which we have not touched. In this chapter, we discuss how psychonarratology could be developed to deal with some of these. First, we recapitulate what we see as the essential ingredients in our approach and summarize some of the specific ideas we have applied to the classic issues in narratology and literary studies. Following that, we discuss some important complications that we have glossed over in our treatment of these issues. Then, we describe some of the other obvious areas in which our treatment has yet to be applied but for which it seems ideally suited. Finally, we mention a few allied domains for which psychonarratology may have implications.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".