What gets categorized counts: Controlled vocabularies, digital affordances, and the international digital humanities conference
Bibliographic record
Abstract
Abstract This article explores how terms are incorporated into the conference submission and review process for the international digital humanities conference. This article provides an overview of the Alliance for Digital Humanities Organizations (ADHO) conference reviewing process and how the controlled vocabulary structures the review process. We show how expanding and rethinking the controlled vocabulary can impact the experience of those who submit, review, and attend the conference. We consider how ConfTool, the submission and reviewing portal used for the international digital humanities conference, processes the controlled vocabulary and algorithmically influences the review of submissions. Ultimately, we advocate for the ability to make intentional and careful changes to conference vocabularies including considering the adoption of a formal ontology. We also suggest that changes to the ConfTool algorithm are needed to ensure a diverse and equitable future for digital humanities.
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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 | 0.105 | 0.347 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.041 | 0.026 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".