Combining Surveys with Seating Sweeps and Observational Data Yields Insights into Physical Space Usage in an Academic Library
Bibliographic record
Abstract
A Review of: Dominguez, G. (2016). Beyond gate counts: Seating studies and observations to assess library space usage. New Library World, 117(5/6), 321-328. https://doi.org/10.1108/NLW-08-2015-0058 Abstract Objective – To propose a new method to assess library space usage and the physical library user experience utilizing multiple data collection techniques. Design – Seating usage studies, surveys, and observation. Setting – Large university in the southern United States. Subjects – Students who physically use the library spaces. Methods – The researcher performed seating sweeps three times a day for one week at time, using a counter to get an accurate headcount of each area of the library. The number of users was recorded on paper and then transferred to Excel. A survey for library patrons was created using Typeform and distributed through both email and in-person. In addition, the researcher created a photo diary to document how students were using the space, particularly creative and flexible uses of the library space. These photos were collected to be shared with library administration. The researcher conducted the study twice, once at each main campus library. Main Results – The initial seating sweeps at one location showed an average of 57 to 85 users engaging in active study, and 57% of users engaged in individual study vs. group study. The sweeping study at the second campus location found that floors designated as quiet floors were the most overcrowded. The researcher found that overall, the actual library use surpassed expected library use. The survey results indicated patron concerns about the lack of available seating, noise policies, uncomfortable furniture, and technology issues such as power outlets and Wi-Fi connectivity. Conclusion – The researcher found that utilizing surveys in addition to observational data provided a more complete picture of the user experience. Photographs also provided depth and texture to the observational data. Based on the findings the librarians and administration plan to upgrade furniture and technology options, as well as make changes to the noise policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.193 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".