Enhancing research and teaching capacity through collaboration: building a UK–Ireland Digital Humanities Association
Bibliographic record
Abstract
Abstract This article discusses the development of a Digital Humanities (DH) Association for the UK and Ireland. It explores processes for evidence gathering, methods for building community through consultation, approaches to defining values and purpose through collaboration, and how to practise openness that is both radical and responsible. It begins by outlining the landscape of DH in the UK and Ireland, highlighting differences and similarities between the two countries. Next, it addresses four key areas of focus in the planning for the new Association: community, consultation, and inclusivity; the importance of advocacy for DH and the role of a DH Association in national policy-making; the centrality of training and the development of career pathways in and from DH; and how to go about implementing a values-led organization. Finally, it reflects on the value of international collaboration in the field of DH, both between Ireland and the UK and among international subject associations and infrastructures.
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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 | 0.095 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.028 | 0.021 |
| Open science | 0.004 | 0.083 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".