Sorting URLs out: seeing the web through infrastructural inversion of archival crawling
Bibliographic record
Abstract
Web archives collections have become important sources for Internet scholars by documenting the past versions of web resources. Understanding how these collections are created and curated is of increasing concern and recent web archives scholarship has studied how the artefacts stored in archives represent specific curatorial choices and collecting practices. This paper takes a novel approach in studying web archiving practice, by focusing on the challenges encountered in archival web crawling and what they reveal about the web itself. Inspired by foundational work in infrastructure studies, infrastructural inversion is applied to study how crawler interactions surface otherwise invisible, background or taken-for-granted aspects of the web. This framework is applied to study three examples selected from interviews and ethnographic fieldwork observations of web archiving practices at the Danish Royal Library, with findings demonstrating how the challenges of archival crawling illuminate the web’s varied actors, as well as their changing relationships, power differentials and politics. Ultimately, analysis through infrastructural inversion reveals how collection via crawling positions archives as active participants in web infrastructure, both shaping and shaped by the needs and motivations of other web actors.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.010 |
| 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".