Is Digital Scholarship Meaningful?: A Campus Study Tracking Multidisciplinary Perceptions
Bibliographic record
Abstract
Increased computational and multimodal approaches to research over the past decades have enabled scholars and learners to forge creative avenues of inquiry, adopt new methodological approaches, and interrogate information in innovative ways. As such, academic libraries have begun to offer a suite of services to support these digitally inflected and data-intense research strategies. These supports, dubbed digital scholarship services in the library profession, break traditional disciplinary boundaries and highlight the methodological significance of research inquiry. Externally, however, these practices appear as domain-specific niches, e.g., digital history or digital humanities in humanities disciplines, e-science and e-research in STEM, and e-social science or computational social science in social science disciplines. The authors conducted a study examining the meaningfulness of the term digital scholarship within the local context at University of Colorado Boulder by investigating how the interpretation of digital scholarship varies according to graduate students, faculty, and other researchers. Nearly half of the definitions (46 percent) mentioned research process or methods as part of digital scholarship. Faculty and staff declined or were unable to define digital scholarship more often than graduate students or post-doctoral researchers. Therefore, digital scholarship as a term is not meaningful to all researchers. We recommend that librarians inflect their practices with the understanding that researchers and library users’ perceptions of digital scholarship vary greatly across contexts.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| 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".