TwinTalks 4: Understanding and Facilitating Remote Collaboration in DH
Bibliographic record
Abstract
While remote collaboration is not new in DH, it has had a profound impact on the DH research, education and community in the past couple of years due to the health, security and financial crises. If absorbed appropriately, it can also prove beneficial in overcoming the various environmental, geographical, mobility and other barriers in the future, making DH more resilient, inclusive and diverse. This is why the main objective of the proposed workshop is to develop a better understanding of the dynamics on the Digital Humanities work floor when researchers, teachers and/or professionals with different areas of competence engage in remote collaboration to solve humanities research questions, and to explore how education and training of humanities scholars, cultural heritage professionals and technical experts can help making remote collaboration across disciplines more efficient and effective, more creative and innovative, and more inclusive and rewarding for all participants. To this end, we invite submissions reporting on all aspects and stages of engaging in remote collaborative research and teaching in DH, including the obstacles encountered and solutions found. We are also welcoming position papers on the role of research infrastructures to better facilitate remote collaboration in DH.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".