Creating order from the mess: web archive derivative datasets and notebooks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For a quarter-century, memory institutions have been preserving web-based content. These web archives have been collected and stored in ARC and WARC (W/ARC) file formats and will form a basis for contemporary histories. Yet, these formats present significant challenges to researchers who wish to access and use web archival data. This is primarily due to the nature of collecting, storing, and providing access to these multifaceted digital objects. In other words, web archives are messy. Applying traditional archival methods of description to digital-born collections is complicated due to issues of provenance, original order, and scale. However, we believe that archival description offers a practical starting point for thinking about access. This paper argues a robust finding aid must extend beyond basic collection-level description to allow for more meaningful interactions with web archives. As such, we propose a reimagining of a traditional finding-aid model into a three-level mode of description to include computational methods, the generation of derivative datasets, and interactive code-rich notebooks. These three factors combine to ultimately contribute to the expanded access and 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it