Annotation Guideline No. 7 (revised): Guidelines for annotation of narrative structure
Bibliographic record
Abstract
Analysis of narrative structure can be said to answer the question “Who tells what, and how?”. The key part of our annotation scheme is related to the “who?”, and to this end we distinguish between narration and fictional dialogue. Furthermore, with respect to the latter we keep track of turns, lines, identities of speakers and addressees, and speech-framing constructions, which provide the narrator’s cues about the circumstances of the speech. We also annotate voice, that is, whether the narrator is ever present in the story or not. Our annotation of the “what?” includes embeddings of narrative transmission levels to capture stories in stories, and embeddings of fictional dialogue to capture characters quoting other characters. Our annotation of the “how?” includes focalization, that is, the perspective from which the narrative is seen and how much information the narrator has access to.
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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 | 0.071 | 0.224 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.096 |
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".