If these crawls could talk: Studying and documenting web archives provenance
Bibliographic record
Abstract
The increasing use and prominence of web archives raises the urgency of establishing mechanisms for transparency in the making of web archives to facilitate the process of evaluating a web archive's provenance, scoping, and absences. Some choices and process events are captured automatically, but their interactions are not currently well understood or documented. This study examined the decision space of web archives and its role in shaping what is and what is not captured in the web archiving process. By comparing how three different web archives collections were created and documented, we investigate how curatorial decisions interact with technical and external factors and we compare commonalities and differences. The findings reveal the need to understand both the social and technical context that shapes those decisions and the ways in which these individual decisions interact. Based on the study, we propose a framework for documenting key dimensions of a collection that addresses the situated nature of the organizational context, technical specificities, and unique characteristics of web materials that are the focus of a collection. The framework enables future researchers to undertake empirical work studying the process of creating web archives collections in different contexts.
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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 | 0.029 | 0.149 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".