Annotation Guidelines For narrative levels, time features, and subjective narration styles in fiction (SANTA 2)
Bibliographic record
Abstract
These guidelines comprise instructions for the usage of a series of markup tags that describe narrative characteristics of fiction. These tags are used to mark disruptions in narration, in the form of narrative level changes, temporal jumps, and instances of subjective narration. The tags are designed to be used in XML, as is the case in the examples in these guidelines, but they can be adapted for other platforms like CATMA. There are six tags: (for a narrative level change, an occurrence of a story within a story), (a flashback), (a flash forward in story time), (stream of consciousness), and (free indirect discourse). The guidelines first describe the narrative concepts represented by each of the tags, with reference to Genette and other narratologists. There follows some detail on how the tags should be used specifically in the encoding of texts, with examples taken from a corpus of modernist fiction. Essentially, the tags should be applied at the points in the text where the relevant instance of narrative disruption begins and ends. This allows them the encoded text to be analysed afterwards to count the frequency of the tags, and the number of words contained within a tag. In this way, the usage of the tags serves as a method for quantifying the extent of narrative disruption in works of fiction.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.022 |
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".