Bibliographic record
Abstract
Literary narratives in which only events are summarized are virtually nonexistent. In most narratives there are characters who speak and think; in fact, casual observation indicates that in many literary texts well over half of the words consist of dialogue or representations of character thoughts. The speech and thought of characters can be used for a wide range of purposes: It can advance the plot, provide direct information about the speaking characters and their reactions as well as indirect information about other characters, present the reader with a variety of perspectives or viewpoints, convey attitudes and judgments of the narrator, and communicate thematic content. Thus, the category of speech and thought intersects with those of narrator, plot, character, and focalization, and it is often impossible to speak of one without alluding to the others. Because of the enormous variety of styles and techniques available to authors for the representation of speech and thought, the study of its forms and uses is a complex problem that, in our view, is still not fully understood. Within the fields of literary scholarship and linguistics, however, there have been a variety of important advances including typologies of speech and thought representation styles. Literary scholars in particular have been sensitive to the crucial issue of the effect of speech and thought representation styles on readers; however, they have been unable to frame hypotheses about reader constructions beyond the limits of purely speculative intuitions.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".