Preface to the Special Issue on Behavioral Queueing Science: The Need for a Multidisciplinary Approach
Bibliographic record
Abstract
Preface to the Special Issue on Behavioral Queueing Science: The Need for a Multidisciplinary Approach Modern service systems are economically important but operationally complex. In “Preface to the Special Issue on Behavioral Queueing Science: The Need for a Multidisciplinary Approach,” Ingolfsson, Mandelbaum, Schultz, and Yom-Tov discuss how this special issue advances the scientific study of queues in services systems by acknowledging the central role of human behavior. Behavioral queueing science requires a multidisciplinary approach, using tools that include mathematical modelling, lab experiments and field studies. Each discipline has strengths and weaknesses, but together they have complementing goals, and jointly they give rise to a scientific paradigm for behavioral queues. Cross-disciplinary work is challenging but necessary. The eleven papers in our special issue show how this can be done successfully while setting an example for future work.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".