Information technology and the humanities scholar: Documenting digital research practices
Bibliographic record
Abstract
Digital tools offer new affordances and methodologies to humanities scholars' research. This study used a constructivist grounded theory approach to examine humanities scholars' research practices, including their use of a wide range of resources and digital technologies. Using in‐depth study, several themes emerged from the research relating to the role of technology in shaping humanities scholars' research practices. The themes include: (a) humanities scholars' research approaches and technology tools; (b) the humanities scholar as tool developer; (c) the role of data preparation as a meta‐level research practice; (d) data visualization versus numeric outputs—one size does not fit all; (e) the importance of flexibility and agency; (f) technology tools in support of the researcher as writer; and (g) working alone/working together—technology tools and collaborative practice. The heterogeneous nature of humanities scholars' research practices are explored and the resulting implications for digital tool design. Two new research practices—tool development and data preparation—are proposed. The diverse digital technologies humanities scholars use support the traditional ways of working within their discipline, as well as creating potential for new scholarly practices.
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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 | 0.117 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.019 | 0.073 |
| Scholarly communication | 0.037 | 0.049 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".