Bibliographic record
Abstract
This paper explores the critical need to document the use of Artificial Intelligence (AI) in humanities research. While AI offers efficiency and analytical power, its application raises concerns about transparency, bias, and reproducibility. Existing documentation frameworks often emphasise technical aspects, overlooking the human and contextual dimensions vital to humanities scholarship. Drawing on cross-disciplinary literature, the paper advocates for integrating paradata (process-related meta-information) to capture both technical and human facets of AI use. It proposes shifting the focus from speculative future needs to documenting the transformation AI is intended to achieve within specific research contexts. Practical strategies include combining automated tools with reflective documentation practices and providing clear explanations of the purpose and expected outcomes of AI use. The paper calls for infrastructural support and a rethinking of documentation sufficiency to enhance understanding, reuse, and accountability in humanities research.
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 | 0.299 | 0.466 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.023 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.038 | 0.046 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".