Bibliographic record
Abstract
In literary studies, it is commonly assumed that the presence of a narrator is one of the features distinguishing fiction from nonfiction (Makaryk, 1993). In natural narratives and nonfictional discourse, the argument goes, the speaker of the utterance speaks directly in his or her own voice, whereas in fictional narratives the utterance is delivered by the mediating voice of a narrator, an entity distinct from the historical author. Although the nature of this distinction and the relationship between author and narrator are matters of debate, virtually all agree on the distinction's importance. Indeed, it is conceivable that even in natural narratives and nonfictional discourse, storytellers or conversational participants may “project” themselves into a speaking function distinct from themselves. But certainly in the case of fictional narratives, the story and all the details pertaining to the story world – characters, events, situations, setting, and so on – are mediated by the voice of a narrator. Inevitably, this mediation affects the reader's responses to the fictional world. The most immediate implication of this fact of narrative is that readers must create a representation of the narrator, that is, a representation of the person who seems to utter the words of the text. Further, in our approach, the reader may represent the narrator as if the reader and the narrator were participating in a communicative situation. The presence, in the mind of the reader, of this communicative situation colors virtually all aspects of the text and its interpretation.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".