From Book Space to People Space: Using Oral History to Celebrate and Reflect on a Major Milestone Anniversary in an Academic Library
Bibliographic record
Abstract
Major milestone anniversaries in libraries provide library administrators, library workers, and the wider community with rich opportunities for both celebration and reflection. In 2023, the University of Toronto Libraries celebrated the 50th anniversary of its flagship library branch, the John P. Robarts Research Library, a monumental Brutalist building that has become an iconic part of the university’s identity and the city skyline. One of the initiatives undertaken to celebrate this milestone was an oral history project to interview past and current library employees and community members about their experiences, thoughts, and reflections about the library. Interviews were conducted with 30 participants, including alumni, faculty and staff, architects, and architectural specialists. The result was a primary source collection of interviews deposited at the University of Toronto Archives for future researchers interested in topics like the history of Robarts Library and the University of Toronto, the history of academic libraries and higher education, the impact of technology on libraries and research, and the history of student life, labor relations, and work at the library. The interviews also generated material for use in 50th anniversary events and programming and proved particularly valuable for the curation of a physical and online exhibition that explored the history, the challenges, and the accomplishments of the library. The oral history project provided an opportunity for the library community to reflect on major themes and drivers of change in its history.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".