Bibliographic record
Abstract
The concept of narrative levels is widely applied in Literary Studies but often based on different theoretical foundations.To operationalise the concept with a reproducible category for a machine learning approach, these guidelines focus on two core definitions of narrative levels, namely Genette's concept of a narrator change and Ryan's proposal of illocutionary and ontological boundaries between levels.We separate the notions of "level" and "narrative" into dedicated subcategories for the narrative level, which reflects a vertical dimension, and the narrative act that encompasses horizontally aligned stories.Furthermore, supplementary aspects, like the boundary type between narrative levels or related phenomena like metanarration and metalepsis, are captured as attributes in conjunction with the annotation category to obtain additional knowledge that might be relevant as training data.The guideline is divided into a first part that discusses narratological theory to define the annotation category as well as the attributes and a second part that gives annotation instructions along with textual examples. PrefaceThese guidelines are an update to the Annotation Guidelines for Narrative Levels and Narrative Acts v1 from 2019.They aim at a clarification of the strategy behind the concepts of narrative levels and narrative acts to reduce the complexity and to achieve a reasonable inter-annotator agreement. ConceptNarrative levels, as proposed by Gérard Genette, aim to describe the relations between an embedded narrative and the diegesis, 1 and indicate a clear hierarchical structure between these diegetic levels.Genette explicitly states his intention to systemize the existing notion of embeddings, which, according to him, lacks "the threshold between one diegesis and another" as well as the possibility to Journal of Cultural Analytics 6 (4).2021.98-139
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.030 |
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".