Bibliographic record
Abstract
Web archiving initiatives around the world capture ephemeral Web content to preserve our collective digital memory. However, unlocking the potential of Web archives for humanities scholars and social scientists requires a scalable analytics infrastructure to support exploration of captured content. We present Warcbase, an open-source Web archiving platform that aims to fill this need. Our platform takes advantage of modern open-source “big data” infrastructure, namely Hadoop, HBase, and Spark, that has been widely deployed in industry. Warcbase provides two main capabilities: support for temporal browsing and a domain-specific language that allows scholars to interrogate Web archives in several different ways. This work represents a collaboration between computer scientists and historians, where we have engaged in iterative codesign to build tools for scholars with no formal computer science training. To provide guidance, we propose a process model for scholarly interactions with Web archives that begins with a question and proceeds iteratively through four main steps: filter, analyze, aggregate, and visualize. We call this the FAAV cycle for short and illustrate with three prototypical case studies. This article presents the current state of the project and discusses future directions.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.189 | 0.183 |
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".