Autumn 2021 Syllabus for Computing Cultural Heritage (CSE 590T / LIS 598A)
Bibliographic record
Abstract
This document is the syllabus for Computing Cultural Heritage, a graduate seminar created and taught by Benjamin Charles Germain Lee at the University of Washington during the Autumn Quarter of 2021, offered in the course catalog in Computer Science & Engineering and Library & Information Science (CSE 590T / LIS 598A). Couse Description: From large-scale systems for searching the web to the datasets that machine learning practitioners utilize to train their models, our collective cultural heritage is in many ways the substrate of computer science. Indeed, cultural heritage practitioners including humanists, librarians, and archivists have been influential in shaping the discourse surrounding the sociotechnical implications of computing. This course explores various topics within computer science through the lens of cultural heritage: data visualization, human-AI interaction, search & discovery, crowdsourcing, web archiving, design & UX, and classification. The goals of this course are two-fold: first, to survey these topics in computer science, and second, to explore how they manifest within the context of cultural heritage. We will cover one topic every week, with the first meeting devoted to the CS-oriented literature for the topic, and the second meeting devoted to the sociotechnical implications of the topic in practice. During these second meetings, we will speak with cultural heritage practitioners at institutions across the country to learn about the roles of computing in their work, research, and stewardship.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.745 | 0.661 |
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".