Bibliographic record
Abstract
I first became aware of the SANTA project at the Digital Humanities conference in Montreal in the summer of 2017. I had just been assigned a 90-student secondyear undergraduate Digital Humanities undergraduate English Literature class, set to begin in January 2018, and I was looking for a group annotation project for my students. In previous iterations of the course, I had carried out several annotation projects focused on the narrative phenomenon of free indirect discourse (FID) in texts by Virginia Woolf and James Joyce. What made these projects successful, from my perspective, was that FID is a complex phenomenon (by definition, a passage in which it is difficult or impossible to say for certain whether a character or narrator is speaking certain words) which is however relatively easy to represent in machine language (for instance, with the TEI element and a few value-attribute pairs). The challenge in the assignment, in other words, was literary rather than technical: while it was easy to learn the TEI tagging, it was hard to say for certain whether a passage from To the Lighthouse was in direct discourse or FID, or to identify who exactly was speaking. To my mind, this made the assignment a meaningful one for my students, teaching them a technical skill while also bringing them into closer contact with the sometimesirresolvable complexities of literary language.
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 | 0.036 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.051 |
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".