Walking the WALK; Facilitating Interdisciplinary Web Archive Collaboration
Bibliographic record
Abstract
The growth of digital sources since the advent of the World Wide Web in 1991, and the commencement of widespread web archiving in 1996, presents profound new opportunities for social and cultural analysis. In simple terms, the 1990s cannot be studied without web archives: they are both primary sources that reflect how people consume and understand media, as well as repositories that document the thoughts, opinions, and activities of millions of everyday people. These are a dream for social historians. \n \nHowever, with all this opportunity comes challenges: large data, the need for interdisciplinary collaboration between historians who might have the questions but not the technical resources or knowledge to work with these sources, and basic questions around what a web archive is and how to access them. Libraries and archives are perfectly positioned to work in this new emerging field that brings together historians, computer scientists, and information specialists. \n \nIn this talk, our speakers will discuss the fruits of one collaboration that has emerged at York University, the University of Alberta, and the University of Waterloo. Bringing together librarians, historians, and computer scientists, as well as an interdisciplinary team of undergraduate and graduate students, this distributed group is developing several web archival analytics projects. \n \nThey work using a combination of centralized and de-centralized infrastructure to run data analytics, store web archives, provide a publicly-facing portal, and collaborate. Ian and Nick will discuss the challenges of working in an interdisciplinary environment, and give insights into how the team has been working through in-detail case studies of their work with http://webarchives.ca, Twitter archiving and analysis, Compute Canada, and warcbase, a web analytics platform. \n \nThe combination of computer scientists and humanists is not always a simple one, but it has proven to be worthwhile.
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 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 | 0.000 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".