Digital Humanities Workshops : Lessons Learned
Bibliographic record
Abstract
<p><em>Digital Humanities Workshops</em> is the first volume to focus explicitly on the most common and accessible kind of training in digital humanities (DH): workshops.</p><p>Drawing together the experiences and expertise of dozens of scholars and practitioners from a variety of disciplines and geographical contexts, the chapters in this collection examine the development, deployment, and assessment of a workshop or workshop series. In the first section, "Where?", the authors seek to situate digital humanities workshops within local, regional, and national contexts. The second section, "Who?", guides readers through questions of audience in relation to digital humanities workshops. In the third and final section, "How?", authors explore the mechanics of such workshops. Taken together, the chapters in this volume answer the important question: why are digital humanities workshops so important and what is their present and future role?</p><p>Digital Humanities Workshops examines a range of digital humanities workshops and highlights audiences, resources, and impact. This volume will appeal to academics, researchers and postgraduate students, as well as professionals working in the DH field. </p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.098 | 0.058 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.022 |
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; both teacher heads agree on what is shown here.
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".