When the medium massages perceptions: Personal (vs. public) displays of information reduce crowding perceptions and outsider mistreatment of frontline staff.
Bibliographic record
Abstract
Crowded waiting areas are volatile environments, where seemingly ordinary people often get frustrated and mistreat frontline staff. Given that crowding is an exogenous factor in many industries (e.g., retail, healthcare), we suggest an intervention that can "massage" outsiders' perceptions of crowding and reduce the mistreatment of frontline staff. We theorize that providing information for outsiders to read while they wait on a personal medium (e.g., a leaflet, a smartphone) reduces their crowding perceptions and mistreatment of frontline staff, compared to providing the same information on a public medium (e.g., poster, wall sign). We report two studies that confirm our theory: A field experiment in Emergency Departments (n = 939) and an online experiment simulating a coffee shop (n = 246). Theoretical and managerial implications are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.003 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".