Library Usage Study, the How and What: A Survey of Space Usage at a Mid-Sized Research Library
Bibliographic record
Abstract
Objective – The research was conducted to understand better how and what spaces are used in a mid-size academic library. Also, the authors were interested in their users' spatial likes and dislikes and why they gravitated to or avoided specific spaces or floors. The authors also found an opportunity to examine recent renovations that added a connector bridge to a first-year student dorm and the subsequent increase in foot traffic to evaluate its success in meeting users' needs for varied and productive study spaces across the building. Methods – The study used a survey to gauge user satisfaction with the library's space and environment for research, study, and collaborative work. The authors hand-distributed a survey with five multiple-choice and three open-response questions to users over three days (Monday-Wednesday) between 10 am - 4 pm, the busiest days and times in a typical week. The collected surveys were sorted and coded in an Excel spreadsheet and uploaded and analyzed in JMP Pro. Results – The 298 completed responses came from undergraduate students (n=281) who visited the first floor, identified as a collaborative study space (n=144). Respondents showed that they visit the library daily (58%, n=173) and weekly (34%, n=104). Most of the survey participants (98%, n=293) indicated that they pursued academic work in quiet spaces they occupied (75%, n=224). Interestingly enough, the noisiest and quietest floors are the areas most avoided, the first floor-collaborative, noisiest space (54%, n=161) and the third floor-designated as quiet space (18%, n=55). The final survey question invited the respondents to "sound off," with 135 responding; 107 (79%) of them opined on improvements to existing study spaces within the library. Conclusion – This research demonstrated that students value the library as a place to study but are critical of excessive noise and overcrowding in the designated collaborative study areas. Academic libraries should consider balance when designing library study spaces. Librarians and space designers should strive to strike an appropriate balance between seating quality and quantity, acceptable noise levels in designated collaborative and quiet study spaces, and the impacts of environmental factors such as printers, food services, exhibits, art displays, restrooms, and walkways through library study spaces within the library.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".