State of Art Museum Libraries: Evolving Practices Since 2016 and Shaping the Next Decade Together
Bibliographic record
Abstract
In 2016, ARLIS/NA published the State of Art Museum Libraries 2016 White Paper, which detailed the roles, issues, and challenges faced by art museum libraries in the United States. The report highlighted how art museum libraries serve as vital partners in their institutions' educational missions by providing authoritative, relevant, and timely research services to both museum constituents and the general public. Despite their critical role, these libraries were facing increasing pressures and needed to justify their value. The report examined the constraints faced by these libraries and offered strategies for overcoming them. Now, five years after the onset of the COVID-19 pandemic, an event that has profoundly reshaped practices across the library field, this panel will present new research and case studies that assess the current state of museum libraries in the United States and Canada. In addition to the 2016 report, research was informed by more recent ARLIS/NA reports, including the 2019 Census of Art Information Professionals and the 2022 Report of the ARLIS/NA Presidential Task Force on Art Libraries and COVID-19. Focusing on the theme of "activating community together," the report's authors presented findings from the field level survey completed by 61 museum libraries and will discuss key findings, including the evolving role of libraries within art museums, institutional support for museum libraries, staffing and hiring practices, work-life balance and workplace culture, the state of diversity, equity, and inclusion initiatives, collection development and management, and emerging trends in user experience. They discussed their research methodology to guide attendees interested in conducting similar studies or expanding on this work. Additional panelists will presented case studies highlighting changes within their own institutions over the past decade, linking the survey data to illustrations of the broader state of the field at the individual institution level.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.041 | 0.024 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".