Understanding computational web archives research methods using research objects
Bibliographic record
Abstract
Use of computational methods for exploration and analysis of web archives sources is emerging in new disciplines such as digital humanities. This raises urgent questions about how such research projects process web archival material using computational methods to construct their findings. This paper aims to enable web archives scholars to document their practices systematically to improve the transparency of their methods. We adopt the Research Object framework to characterize three case studies that use computational methods to analyze web archives within digital history research. We then discuss how the framework can support the characterization of research methods and serve as a basis for discussions of methods and issues such as reuse and provenance. The results suggest that the framework provides an effective conceptual perspective to describe and analyze the computational methods used in web archive research on a high level and make transparent the choices made in the process. The documentation of the research process contributes to a better understanding of the findings and their provenance, and the possible reuse of data, methods, and workflows.
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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 | 0.098 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.030 | 0.033 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".