In defense of sandcastles: Research thinking through visualization in digital humanities
Bibliographic record
Abstract
Abstract Although recent research acknowledges the potential of visualization methods in digital humanities (DH), the predominant terminology used to describe visualizations (prototypes and tools) focuses on their use as a means to an end and, more importantly, as an instrument in the service of humanities research. We introduce the sandcastle as a metaphorical lens and provocative term to highlight visualization as a research process in its own right. We argue that building visualization sandcastles provides a holistic approach to cross-disciplinary knowledge generation that embraces visualization as (1) an aesthetic provocation to elicit critical insights, interpretation, speculation, and discussions within and beyond scholarly audiences, (2) a dynamic process wherein speculation and re-interpretation advance knowledge within all disciplines involved, and (3) a mediator of ideas and theories within and across disciplines. Our argument is grounded in critical theory, DH, design, human–computer interaction, and visualization, and based on our own research on an exceptional literary collection. We argue that considering visualizations as sandcastles foregrounds valuable insights into the roles of visualization as a mindset, methodology, and praxis within humanities research and beyond.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.055 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".