All WARC and no playback: The materialities of data-centered web archives research
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
This paper examines the Web ARChive (WARC) file format, revealing how the format has come to play a central role in the development and standardization of interoperable tools and methods for the international web archiving community. In the context of emerging big data approaches, I consider the sociotechnical relationships between material construction of data and information infrastructures for collecting and research. Analysis is inspired by Star and Griesemer's historical case of the Museum of Vertebrate Zoology which reveals how boundary objects and methods standardization are used to enroll actors in the work of collecting for natural history. I extend these concepts by pairing them with frameworks for studying digital materiality and the representational qualities of data artifacts. Through examples drawn from fieldwork observations studying two data-centered research projects, I consider how the materiality of the WARC format influences research methods and approaches to data extraction, selection, and transformation. Findings identify three modalities researchers use to configure WARC data for researcher needs: using indexes to support search queries, constructing derivative formats designed for certain types of analysis, and generating custom-designed datasets tailored for specific research purposes. Findings additionally reveal similarities in how these distinct methods approach automated data extraction by relying upon the WARC's standardized metadata elements. By interrogating whose information needs are being met and taken into account in the design of the WARC's underlying information representation, I reveal effects on the emerging field of web history, and consider alternative approaches to knowledge production with archived web data.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.011 |
| 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