The Typewriter Under the Bed: Introducing Digital Humanities through Banned Books and Endangered Knowledge
Bibliographic record
Abstract
In 2017, I taught an Introduction to Digital Humanities course for undergraduate students at the University of Toronto. The course’s unifying theme was banned books. What moved me to focus the course in this way was the illegal typewriter that lived under my childhood bed: I grew up in formerly communist Eastern Europe, where typewriters were tightly controlled by the government. Yet my family owned an illegal, unregistered typewriter, hidden under my bed behind the off-season clothes, because they saw the ability to write and disseminate one’s thoughts as a technology of survival.In the Intro to DH course, students explored the intellectual landscape of the digital humanities by thinking about banned books throughout history. They examined early printed books of astronomy; early printed books of the lives of saints; illicitly typewritten and photographed Soviet samizdat; endangered climate change research data rescued by the Internet Archive; and American Library Association data about banned and challenged books for children and young adults. This article reflects on using the lens of banned books and endangered knowledge to focus an Introduction to DH course and encourage students to interrogate critically how a variety of technologies—from codex to printing press to typewriter to the internet—create, transmit, preserve, and repress knowledge and cultural memory.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".