Living Labs and the DH Centre: Lessons for Each from the Other
Bibliographic record
Abstract
The digital humanities (DH) has a long and successful history of creating, using, and maintaining DH centres, as evidenced by the vast centerNet network. Furthermore, some of the most successful centres are constantly evolving in form and function. In this paper, we propose that the next phase in the evolution of the DH centre may involve a related phenomenon from the design research community, called the ‘Living Lab.’ The European Network of Living Labs describes them as dedicated to open forms of design for social good: ‘Living Labs (LLs) are defined as user-centred, open innovation ecosystems based on a systematic user co-creation approach, integrating research and innovation processes in real-life communities and settings.’ Current member labs deal with topics ranging from health and well-being (52%) to mobility (14%), but there are few that focus on issues central to DH, such as open social scholarship. We argue that incorporating more DH into the Living Labs network, and more Living Labs into DH centres, would benefit everyone involved. Specifically, DH labs could benefit from Living Labs’ experience with complex problems, and Living Labs could benefit from DH centres’ experience producing research.
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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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.038 |
| Scholarly communication | 0.031 | 0.072 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.018 | 0.024 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".