Should we really ‘hermeneutise’ the Digital Humanities? A plea for the epistemic productivity of a ‘cultural technique of flattening’ in the Humanities.
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.
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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 | 0.025 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.120 |
| Scholarly communication | 0.032 | 0.067 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".