Conceptual Forays: A Corpus-based Study of “Theory” in Digital Humanities Journals
Bibliographic record
Abstract
The status of theory in the Digital Humanities (DH) has been the subject of much debate. As a result, we find different theory narratives competing and entangled with each other. If at all, these narratives can only be grasped and examined from a somewhat detached perspective. Here, we attempt to investigate these elusive narratives by means of a conceptual history approach. In doing so, we define different theory dimensions, ranging from specific cultural and literary theory frameworks to more generic uses of the concept of _theory_. We examine the use and semantic changes of these theory notions in a large corpus of DH journals. Using a mixture of heuristic methods and approaches from the field of distributional semantics, we aim to create tellable conceptual stories of DH theory.
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.021 | 0.031 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".