Emerging Best Practices for Managing Online Exhibits: Survey Report
Bibliographic record
Abstract
ABSTRACT Many libraries, archives, museums, and other institutions provide online exhibits to serve as surrogates for or to supplement in-person/physical exhibits. Online exhibits may provide more detailed information about a physical exhibit or may stand alone. While online exhibits can have more permanence than physical exhibits, they can be difficult to support in the long term, requiring regular updates and maintenance. Online exhibits may lose context when creators and supporting staff with institutional memory leave the host institution. Migrating a legacy exhibit to a current version, a different platform, and/or a new server can also prove challenging. To continue serving patrons optimally, online exhibits must remain fully accessible and be regularly reviewed for accuracy and utility. As rapidly developing technology presents new opportunities to connect virtually with researchers and the public, online exhibits should be reviewed and supported to supplement institutional and programmatic goals, especially if they are still pertinent to the mission of the institution. This article investigates current policies and practices for evaluating, managing, and sustaining online exhibits for archives, museums, and libraries in the United States and Canada. It includes emerging best practices based on survey findings.
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 | 0.060 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".