Bibliographic record
Abstract
The framing of Guideline VI within the pedagogical situation of a class on “Digital Methods in Literary Studies” is helpful in pointing out some of the ways in which the theory and practice of annotation can serve students of literature, as well as eventually contributing to computational analysis. Above all, annotation necessitates firm decisions, as the authors describe: ”Rather than let an ambiguous text stay ambiguous, they simply had to decide for one option in order to be able to annotate a passage and had to justify their choice with reference to the whole text or to adapt the guidelines in order to address and document the ambiguity” (3). This remark highlights the challenge in developing annotation guidelines so that they can be used consistently by different communities of users without modifications. The authors note several points of debate within the class that are relevant to the overall shared task and its evaluation: the feasibility of developing annotation guidelines that could be applied to a wide range of literary texts; the involved levels of textual interpretation that some kinds of annotation require; and the effect of prior study or knowledge on an annotator’s ability to discern or interpret narrative levels. As the shared task proceeds, it may be necessary to specify the applicability of the annotation guidelines to works from particular genres, time periods, or languages.
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 | 0.065 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".