Conceptualizing aggregate-level description in web archives
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
Web archives collections are often excluded from archival science discussions, and their description instead focuses on bibliographic approaches to item-level metadata. This article argues that web archives are best understood using approaches of archival description, focusing on a case study of the Danish Netarchive, a long-running national web archive. By capturing and preserving web sites for the purposes of legal deposit, the Netarchive creates and maintains historical records of the web. Examining the Netarchive’s systems and activities through the lens of archival representation, this article develops a typology of representational artifacts that support this work, including the use of database entities, wiki documentation, classification and management via Jira issues, and codes, identifiers, and structures embedded in network protocols themselves. The analysis considers how meaningful aggregations can be understood via these representational schemes, systems and architectures, and how the nature of born-networked records challenges concepts of singular, hierarchical orderings of records aggregations. The closing discussion proposes new modes of description that address these multiple interconnected systems, and raises questions about what this might mean for aggregate-level description in the context of digital and born-networked records more broadly.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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