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.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".