Case Article—Potty Parity: Stadium Restroom Design
Bibliographic record
Abstract
In view of the long wait times for women and the lack of accessibility for LGBTQ+ individuals when they use restrooms, this case provides a set of analytical tools to evaluate wait time disparity among users for different restroom configurations. A stadium manager who faces complaints about excessive restroom wait times aims to retrofit the restroom layout to improve both efficiency, measured in terms of wait time, and fairness, measured in terms of totalitarian and Rawlsian scores. Given that customers have diverse preferences over the use of restroom types, in three modules, students learn to (i) evaluate queuing parameters for a mix of heterogeneous populations, (ii) evaluate queuing metrics for various restroom layouts and discuss their wait time disparities, and (iii) evaluate and discuss the fairness of access to restroom facilities from a diversity, equity, and inclusion (DEI) perspective. By completing this case, students gain an understanding of service systems, learn about process flexibility concepts, and become familiar with DEI concepts and measures. The primary objectives of the case for students are to understand the trade-offs between efficiency and fairness, develop an understanding of multiobjective problems, and improve their skills in employing queuing concepts and tools. History: This paper has been accepted for the INFORMS Transactions on Education Special Issue on Diversity, Equity and Inclusion in OR/MS Classrooms. Supplemental Material: The Teaching Note and Excel files are available at https://www.informs.org/Publications/Subscribe/Access-Restricted-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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".