What Does Reproducibility Look Like for DH Projects?
Bibliographic record
Abstract
While frameworks for open science have long advocated for reproducibility in research, reproducibility within the digital humanities continues to be challenging to achieve. This is particularly salient in the case of DH projects whose primary output is a front-facing web resource, of which all component parts—the data, the code, the interface, and the user experience—are all key to the project’s scholarly contribution. While institutional repositories and other digital infrastructure are well equipped to store and preserve a wide variety of outputs, DH projects tend to be stored in fragments and not in ways that allow future users to “spin up” or “reproduce” the form and function of the project as it was initially created. This presentation outlines our approach at SFU Library's Digital Humanities Innovation Lab to creating sustainable and reproducible DH projects that go beyond archiving of source code and/or data. Drawing on the Endings Principles as well the recent Digits report on containerization as scholarly product, this lightning talk will describe the DHIL’s multi-faceted strategy—including data deposits, static websites, web archives, and multiple generated containers—for sustaining, openly sharing, and reproducing research outputs in the digital humanities. In particular, we will discuss how we have automated much of this process to demonstrate the feasibility of this approach while also outlining future directions and recommendations for institutional repositories and other institutions to better enable the open sharing of DH projects.
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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 | 0.313 | 0.581 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.016 | 0.042 |
| Scholarly communication | 0.057 | 0.079 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".