Bibliographic record
Abstract
This paper discusses the state-of-the-art in digital “genetic” editing, that is the philological analysis (and presentation) of the processes behind the creation of literary texts. Research on such processes is mainly based on draft manuscripts or typescripts that authors have left behind intentionally or accidentally. Creative note-taking, revisions, proof-readings, cross-linkings and additional material makes them a complex and interwoven set of data requiring specific analytic tools and reading and research environments for both general and specialist readers and users to understand them better. The paper traces the idea of pre-electronic genetic editing and the significant changes it is undergoing in the digital era. It compares two editorial projects on renowned authors, one in print and one digital: the so-called ‘Frankfurt edition’ of Friedrich Hölderlin, and the Samuel Beckett Digital Manuscript Project. The paper discusses these in particular as “reading environments” (or user interfaces) designed for “critically experiencing” authorial writing processes in both the print and the digital medium.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".