Annotation Guideline No. 6: SANTA 6 Collaborative Annotation as a Teaching Tool Between Theory and Practice
Bibliographic record
Abstract
These guidelines were developed in our seminar “Digital Methods in Literary Studies”, which was aimed at M.A. students and advanced B.A. students. At the beginning of the seminar, students were introduced to the aims and challenges of digital annotating in general as well as to different narratological theories (including Genette, Ryan, Nelles, and Füredy). Due to its narratologically challenging nature, Mary Shelley’s Frankenstein was chosen as a text against which we could test our guidelines and which triggered their modification. In Frankenstein many changes (e.g. of narrator and narratee) occur at the beginning of chapters. Even though such changes can, of course, also be found in the middle of chapters, annotators should pay special attention to the beginning of chapters, because they often coincide with a change in narrator, narratee, or narrated world.
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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 | 0.054 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.027 | 0.035 |
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".