Bibliographic record
Abstract
In this contribution we discuss what we call the "digital humanities-as-structuralism" narrative for the case of computational literary studies. To better understand the entailed criticism, we start with some background for the non-computational aspects in this narrative. First, we single out major criticisms against structuralism. We then introduce a general and theory-independent model of literary text analysis and discuss hypothesis development and justification in literary studies. This builds the ground for our analysis of structuralism criticisms in computational literary studies. In our discussion of the "digital humanities-as-structuralism" narrative, we examine the use of computational methods for the exploration and confirmation of interpretation hypotheses and its potential relation to structuralist issues. We argue that the "digital humanities-as-structuralism" narrative may be productive where it cautions against reductionist approaches, but it is not appropriate for describing exploratory or partial approaches and the presentation of their findings. There, the computational approaches should rather be seen as enabling connectivity and fostering the joint endeavor of understanding.
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 | 0.031 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.019 | 0.039 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".