User-focused, User-led: Space Assessment to Transform a Small Academic Library
Bibliographic record
Abstract
Abstract Objective – By collecting and analyzing evidence from three data points, researchers sought to understand how library spaces are used. Researchers have used results for evidence based decision making regarding physical library spaces. Methods – Undergraduate researchers, sociology faculty, and librarians used mixed-methods to triangulate findings. Seating sweeps were used to map patrons’ activities in the library. Student-led focus groups discussed patterns of library use, impressions of facilities, and library features and services. The final step included a campus survey developed from seating sweeps and focus group findings. Results – Seating sweeps showed consistent use of the library's main level Learning Commons and upper level quiet spaces; the library’s multipurpose lower level is under-utilized. Students use the main level of the library for collaborative learning, socializing, reading, and computer use. Students use the upper level for quiet study and group work in study rooms. Focus group findings found library use is task-specific. For example, a student may work with classmates on a project using the main level Learning Commons during the day, and then come back at night to use the quiet floor for test preparation. Survey responses highlighted areas in which the library is deficient. For example, respondents cited crowdedness, noise levels, and temperature concerns. Conclusion – These data offer empirical evidence for library space needs. Some data aligns with previous space studies conducted at this library: access to power outlets, lighting, noise, and an outdated environment. Evidence also supports anecdotal concerns of crowding, graduate students lacking designated study space, and the need for quiet study space away from group study space.
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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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".