One library’s quick response to QR technology for the arts
Bibliographic record
Abstract
The University of Guelph McLaughlin Library has its own art collection and also displays over 100 works on permanent loan from the campus MacDonald Stewart Art Centre. In recent years the library has created a large public space on the main floor utilized as a Town Square, a place of interaction between the community and the academic and scholarly endeavours of the university. Among many public activities this space has been used to host various art exhibits which often include tours of the library art collection. Librarians at the University of Guelph are committed to promoting our collection to support academic programs and provide access to original art to community users. We are presently engaged in a project to use QR labelling technology to increase the visibility of and accessibility of our art collection. This enhanced labelling links viewers to online artists' biographical information and will open the collection to faculty for teaching purposes and provide enriched learning opportunities for students to engage with both historical and contemporary art. Access for local artists and community visitors will also benefit from this approach to experiencing our art collection. This poster addresses the practical considerations of QR labelling a collection, the technology, expertise, and resources required and cost in materials and time. Information is provided to illustrate collection promotion opportunities with the use of QR codes for enhanced self guided tours of and examples are given of ways to incorporate QR collection information into academic art history and appreciation courses.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.386 | 0.209 |
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".