Bibliographic record
Abstract
“Let me tell you a story.” The proposed guidelines suggest that this phrase serve as the heuristic that readers supply at the beginning of any possible embedded narrative to identify a shift in narrative frames or levels. (The difference between “frame” and “level,” although perhaps confusing in the history of narratology, does not seem like an important distinction at this stage of the project.) This simple phrase, the author suggests, can replace a field of narrative theory they feel would “simply confuse my student annotators.” However simple the phrase might seem, however, it, in fact, conceals a number of key narratological issues: focalization, temporal indices, diction / register, person, fictional paratexts, duration, and, no doubt, others. The question for the guidelines is whether one can leapfrog the particularity of these issues if students use the above phrase to annotate texts with XML tags and produce operational scripts that identify the nested narratives. As it currently stands, students seem capable of learning the basic idea of nested narratives and tagging changes in narrative frames, but there are no real results to confirm the project’s success, as the author reports they are not yet able to confirm any inter-annotation agreement.
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 | 0.073 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".