Bibliographic record
Abstract
The guideline under review builds on the acquired knowledge of the field of narrative theory. Its main references are to classical structuralist narratology, both in terms of definitions (Todorov, Genette, Dolezel) and by way of its guiding principles, which strive for simplicity, hierarchy, minimal interpretation and a strict focus on the annotation of text-intrinsic, linguistic aspects of narrative. Most recent attempts to do “computational narratology” have been similarly “structuralist” in outlook, albeit with a stronger focus on aspects of story grammar: the basis constituents of the story are to some extent hard-coded into the language of any story, and are thus more easily formalized. The present guideline goes well beyond this restriction to story grammar. In fact, the guideline promises to tackle aspects of narrative transmission from the highest level (author) to the lowest (character), but also demarcation of scenes at the level of plot, as well as focalisation. Thus, the guideline can be said to be very wide in scope.
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 | 0.027 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".