Pop-up exhibits as an outreach tool: Connecting academic and public audiences with library resources
Bibliographic record
Abstract
Subject specialty libraries like the Earth Sciences and Map Library at UC Berkeley thrive on active user communities, but promoting awareness of resources and services can be slow and time-consuming. In Fall 2014, two new librarians there introduced a series of monthly pop-up exhibits called “Maps and More” in order to help build that community. Now in their third year, the show-and-tell sessions are designed to lure visitors into the library and spark new connections among students, researchers and librarians, and renewed engagement with library collections. The librarians surveyed participants in spring 2015 and fall 2016 to assess the impact of “Maps and More” in terms of visitors’ perception of the events and awareness of library services and materials. One significant finding from the spring 2015 survey is that a quarter of respondents had never been to the Earth Sciences and Map Library before the session. The exhibits draw in participants from a range of departments, including significant numbers from Seismology, Earth and Planetary Science, Geography, as well as members of the public. Sessions held on Cal Day -- the university’s annual public open house with 30-40,000 public visitors -- were particularly good outreach experiences. Partnerships with other units have great potential for expanding the reach of these events. The Maps and More exhibits have proven to be a successful outreach tool for raising the profile of the library collections and services and helping users understand maps as research materials.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 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".