Prototyping Across the Disciplines
Bibliographic record
Abstract
This article pursues the idea that within interdisciplinary teams in which researchers might find themselves participating, there are very different notions of research outcomes, as well as languages in which they are expressed. We explore the notion of the software prototype within the discussion of making and building in digital humanities. The backdrop for our discussion is a collaboration between project team members from computer science and literature that resulted in a tool named TopoText that was built to geocode locations within an unstructured text and to perform some basic Natural Language Processing (NLP) tasks about the context of those locations. In the interest of collaborating more effectively with increasingly larger and more multidisciplinary research communities, we move outward from that specific collaboration to explore one of the ways that such research is characterized in the domain of software engineering—the ISO/IEC 25010:2011 standard. Although not a perfect fit with discourses of value in the humanities, it provides a possible starting point for forging shared vocabularies within the research collaboratory. In particular, we focus on a subset of characteristics outlined by the standard and attempt to translate them into terms generative of further discussion in the digital humanities community.
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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 | 0.047 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".