Bibliographic record
Abstract
Purpose To describe how academic libraries can support digital humanities (DH) research by leveraging established library values and strengths to provide support for preservation and access and physical and digital spaces for researchers and communities, specifically focused on cultural heritage collections. Design/methodology/approach The experiences of the authors in collaborating with DH scholars and community organizations is discussed with references to the literature. The paper suggests how research libraries can use existing expertise and infrastructure to support the development of digital cultural heritage collections and DH research. Findings Developing working collaborations with DH researchers and community organizations is a productive way to engage in impactful cultural heritage digital projects. It can aid resource allocation decisions to support active research, strategic goals, community needs and the development and preservation of unique, locally relevant collections. Libraries do not need to radically transform themselves to do this work, they have established strengths that can be effective in meeting the challenges of DH research. Practical implications Academic libraries should strategically direct the work they already excel at to support DH research and work with scholars and communities to build collections and infrastructure to support these initiatives. Originality/value The paper recommends practical approaches, supported by literature and local examples, that could be taken when building DH and community-engaged cultural heritage projects.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.170 | 0.083 |
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".