Touch Tables for Special Collections Libraries: Curators Creating User Experiences
Bibliographic record
Abstract
This article describes the implementation of touch table technology for McGill University Library’s special collections. The touch table was used by the Osler Library of the History of Medicine and the Marvin Duchow Music Library to create audiovisual exhibits to accompany traditional exhibition display cases. Each exhibition curator used a different software platform to create his or her touch table experience. This article explores the introduction of what is now a common technology in museums into the library setting and the attendant challenges, such as the need to create attractive and user-friendly experiences with limited resources and programmer time available. In particular, the article explores the library’s choices of software and hardware, providing lessons learned as well as some preliminary recommendations of best practices. It also analyzes the ways in which the touch table has proven to be an excellent addition to the library’s exhibition spaces, including its ability to unite disparate resources from multiple branch libraries, to act as a new point of librarian-faculty collaboration, and to display nontraditional items from library collections, such as recorded musical performances and archival video footage.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".