Should we really ‘hermeneutise’ the Digital Humanities? A plea for the epistemic productivity of a ‘cultural technique of flattening’ in the Humanities.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Why are the Digital Humanities a genuine part of the Humanities? Attempts are currently being made by arguing that computational methods are at the same time hermeneutic procedures (‘screwmeneutics’, ‘hermenumericals’): computation and hermeneutics were mixed. In criticizing this fusion of ‘literacy’ and ‘numeracy’, it is argued that what really connects the classical Humanities and the Digital Humanities is methodologically based on the ‘cultural technique of flattening’ and not on hermeneutics. The projection of spatial and non-spatial relations onto the artificial flatness of inscribed and illustrated surfaces forms a first-order epistemic and cultural potential in the history of the Humanities: diagrammatic reasoning, the visualizing potential of writings, lists, tables, diagrams, and maps, the sorting function of alphabetically ordered knowledge corpora have always shaped and determined basic scholarly work. It is this ‘diagrammatical’ dimension to which the Digital Humanities are linked to Humanities in general. The metamorphosis of texts, pictures, and music into the surface configurations of machine-analyzable data corpora opens up the possibility of revealing latent and implicit patterns of cultural artifacts, and practices that mostly are not accessible to human perception. The quantifying, computational methods of the Digital Humanities operate like computer-generated microscopes and telescopes into the cultural heritage, ongoing cultural practices, and even the culturally unconscious.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it