Beyond validation: Using programmed diagnostics to learn about, monitor, and successfully complete your DH project
Bibliographic record
Abstract
Abstract Digital humanities projects have long relied on various schema languages—chiefly, RELAX NG and Schematron—for validating the XML documents in their data collections; however, these languages are limited in their ability to check for consistency, coherence, and completeness across the entire project. In our work as part of “Endings”, an umbrella project that comprises four diverse digital edition projects from different fields, we have developed a methodology for checking and enforcing correctness, completeness, and coherence across the entire document set. The following article describes the various stages (what we term “levels”) of our diagnostics process, all of which are driven by XSLT (Extensible Stylesheet Language Transformations) stylesheets, and produce a human readable report. These levels include checks for referential integrity, correct entity tagging, and potential duplicates in the data set. Using examples from the Endings projects, we show how diagnostic processes not only ensure correctness in the data set, but can also aid in determining project milestones and completion dates. Diagnostics, we argue, are thus a crucial extension to schema-based validation for complex digital projects and can provide concrete ways for digital humanities projects to enforce coherence and consistency and track their progress toward completion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".