Bibliographic record
Abstract
Purpose In Digging into Data 3 (DID3) (2014-2016), ten funders from four countries (the USA, Canada, the UK, and the Netherlands) granted $5.1 million to 14 project teams to pursue data-intensive, interdisciplinary, and international digital humanities (DH) research. The purpose of this paper is to employ the DID3 projects as a case study to explore the following research question: what roles do librarians and archivists take on in data-intensive, interdisciplinary, and international DH projects? Design/methodology/approach Participation was secured from 53 persons representing eleven projects. The study was conducted in the naturalistic paradigm. It is a qualitative case study involving snowball sampling, semi-structured interviews, and grounded analysis. Findings Librarians or archivists were involved officially in 3 of the 11 projects (27.3 percent). Perhaps more importantly, information professionals played vital unofficial roles in these projects, namely as consultants and liaisons and also as technical support. Information and library science (ILS) expertise helped DID3 researchers with issues such as visualization, rights management, and user testing. DID3 participants also suggested ways in which librarians and archivists might further support DH projects, concentrating on three key areas: curation, outreach, and ILS education. Finally, six directions for future research are suggested. Originality/value Much untapped potential exists for librarians and archivists to collaborate with DH scholars; a gap exists between researcher awareness and information professionals’ capacity.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
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".