An Analysis of the Effect of Saturday Home Football Games on Physical Use of University Libraries
Bibliographic record
Abstract
Objective – Library science literature lacks studies on the effect of external events on the physical use of libraries, leaving a gap in understanding of would-be library patrons’ time use choices when faced with the option of using the library or attending time-bound, external events. Within academic libraries in about 900 colleges and universities in the US, weekend time use may be affected by football games. This study sought to elucidate the effect of external events on physical use of libraries by examining the effect of Saturday home football games on the physical use of the libraries in a large, academic institution. Methods – This study used a retrospective, observational study design. Gate count data for all Saturdays during the fall semesters of 2013-2018 were collected for the two primary libraries at East Carolina University (main campus’ Academic Library Services [ALS] and Laupus, a health sciences campus library), along with data on the occurrence of home football games. The relationship between gate counts and the occurrence of home football games was assessed using an independent samples t-test. Results – Saturday home football games decreased the gate count at both ALS and Laupus. For ALS, the mean physical use of the library decreased by one third (34.4%) on Saturdays with a home game. For Laupus, physical use of the library decreased by almost a quarter (22%) on Saturdays with a home game. Conclusion – Saturday home football games alter the physical use of academic libraries, decreasing the number of patrons entering the doors. Libraries may be able to adjust staffing based on reduced use of library facilities during these events.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".