The Labor Behind DH Data Complexity: Balancing Priorities as a Real-Life Researcher
Bibliographic record
Abstract
Digital Humanities practitioners vocally praise the creation, re-use, and critique of complex and nuanced data. I connect this well-founded goal of rich humanistic data to the increased amounts of labor required different roles in such data's curation, including the labor that data will ultimately require from its audiences down the road. I argue that this net increase in different project labor forms can be a virtue, but only when that labor is properly planned for, distributed, credited, and compensated. Originally presented at the May 21, 2021 workshop "Making Research Data Public: Workshopping Data Management for Digital Humanities," hosted by the University of Ottawa Library.
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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 | 0.296 | 0.347 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.038 | 0.114 |
| Scholarly communication | 0.070 | 0.070 |
| Open science | 0.006 | 0.048 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier 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".