On the Theory of Narrative Levels and Their Annotation in the Digital Context
Bibliographic record
Abstract
The article was written in the context of a Shared Task on the Analysis of Narrative levels Through Annotation (“SANTA”) which was published as a first draft in 2019. This revised version is based on further discussion on the formalization of the narratological concept of ‘narrative level.’ We firstly discuss the theory of narrative levels in literary studies, secondly derive features for the identification of narrative levels and finally develop guidelines for their annotation. An essential finding of the theoretical work lies in connecting the concept of ‘narrative level’ to the narrator. By identifying different types of narrators, we are able to enumerate and categorize different scenarios for the emergence of new levels in narrative texts. Hereby, the article does not remain restricted to prototypical cases, but also deals with rare and problematic cases. Overall, our goal is to provide a theoretical reflection on narrative levels and to create accurate guidelines for its recognition. The method of approaching the phenomenon through annotation has proven to be extremely fruitful particularly in identifying the boundaries of the narrative levels.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.017 | 0.036 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".