What digital humanists don't know about scholarly editing; what scholarly editors don't know about the digital world
Bibliographic record
Abstract
This paper presents, somewhat polemically, the view that textual scholars need to immerse themselves in the digital world, and take full responsibility themselves for the digital editions they make. This requires a rethinking of the model of collaboration between textual scholar and digital humanist which has reigned for twenty years: what the paper calls the 'one scholar/one project/one digital humanist' model. The paper should be read with a companion blog, at http://scholarlydigitaleditions.blogspot.com/2013/07/why-digital-humanists-should-get-out-of.html, which elaborates an alternative model for collaboration between textual scholars and digital humanists (basically: many scholars/many projects/many digital humanists), and advocates too the widespread adoption of Creative Commons attribution share-alike licences (without the toxic 'non-commercial' restriction) for edition materials, and for their availability through open APIs, independent of any one interface. Joris van Zundert's blog at http://brandaen.huygensinstituut.nl/?p=497 (note his comment on his own blog) also contains arguments which should be read alongside this paper.
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 | 0.035 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.041 | 0.073 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".