Derivative data for Web Archives for Longitudinal Knowledge (WALK)
Bibliographic record
Abstract
These are derivative files generated by the Web Archives for Longitudinal Knowledge (WALK) project, which ran between 2016 and 2018. WALK was an interdisciplinary project spearheaded by scholars at York University, the University of Waterloo, and the University of Alberta. The project's goal was to bring together major Canadian web archive holdings and provide researcher access to search indexes and derivative files, including plain text, network diagrams, and domain frequency information. These will be useful to digital humanists who want to work with text at scale or the hyperlink networks of large parts of the archived Web. Six universities participated: the University of Toronto, University of Alberta, University of Victoria, University of Winnipeg, Dalhousie University, and Simon Fraser University. These files reflect the state of their public web archives in late-2017 to mid-2018. Each xz file contains: derivative files for a given collection, a GraphML file which you can load with Gephi (it will not have any basic layouts or transformations done to it, requiring you to do so manually), a csv file that explains the distribution of domains within the web archive, and a txt file that contains the plain text extracted from HTML documents within the web archive. You can find the crawl date, full URL, and the plain text of each page within the txt file. It may also contain a GEXF file which you can load with Gephi. It will have a basic layout courtesy of our GraphPass program, allowing you to see major nodes and communities in the network. This project has evolved into the Archives Unleashed Project. Information on Archives Unleashed and the WALK project can be found at https://archivesunleashed.org and on our blog at https://news.archivesunleashed.org.
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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.133 | 0.086 |
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".