What's cached is prologue: Reviewing recent web archives research towards supporting scholarly use
Bibliographic record
Abstract
ABSTRACT Web archives are essential to support historical scholarship in the online age. Research on web archives spans many disciplines, often requiring domain‐specific expertise. The wide‐ranging nature of the literature makes it difficult to obtain a current overview of the field, but this view is needed to identify which core challenges define the field, and assess the different approaches taken to address them. This paper provides such a review of the current landscape of web archives research, focusing on addressing the common challenges faced to support scholarly use of archived web materials. The analysis describes three challenges and identifies key concepts and current approaches for each: (1) how to organize and select from web archives collections; (2) how to critically examine these sources; and (3) how to approach ethics and consent for using archived web materials. The discussion addresses open questions and tensions, highlighting the sociotechnical nature of these challenges and revealing opportunities to apply existing work from the body of knowledge of information studies. It concludes with several recommendations for future research directions to support scholarly use of web archives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".