Mixing Digital Humanities and Applied Science Librarianship: Using Voyant Tools to Reveal Word Patterns in Faculty Research
Bibliographic record
Abstract
Awareness of faculty research interests is an important aspect of a subject librarian's responsibilities. This paper illustrates the potential of Voyant Tools, an application in wide use among digital humanities researchers, to reveal word patterns in the research output of applied science faculty. A corpus of recent article citations from Web of Science from two engineering departments was obtained, and the articles' title field was extracted and uploaded to the application. The exercise indicated that articles on fuel cells dominates the research output of one department, and articles on optical coherence tomography dominates the other. Both the corpus of citations and its visualizations in Voyant Tools contribute to librarians' knowledge of their departments and historical spending patterns on specialized resources. This knowledge can be used in professional practice, including collection development and instruction. As academic subject areas become increasingly complex and multidisciplinary, this paper encourages librarians to engage with Voyant Tools to better understand the specialized language and concepts of these evolving fields.
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.033 | 0.035 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".