Words, Words, Words: How the Digital Humanities Are Integrating Diverse Research Fields to Study People
Bibliographic record
Abstract
The rapidly developing field of digital humanities (DH) is showing how unprecedented volumes of data such as written expression can be studied to reveal new insights into humans and, therefore, into individual and collective experiences within and across societies. Scholars from disciplines such as literature and history are collaborating with scientists from disciplines such as statistics and computer science. Moreover, these interdisciplinary teams often reach beyond campuses to companies as well as local, national, and international public and nonprofit institutions. Surprisingly, the computational research that began in the humanities in the 1950s did not develop an important presence within mainstream scholarship until half a century later. The DH experiences thus far reflect the complexity of both human expression and research collaborations across diverse fields and sectors. Learning from past successes and failures will help meet today's data analytic challenges and prepare us for opportunities in statistical applications ranging from literary studies and cybersecurity to business intelligence and health indicators.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.022 | 0.050 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".