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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".