Physical and situational inequality on airplanes predicts air rage
Bibliographic record
Abstract
We posit that the modern airplane is a social microcosm of class-based society, and that the increasing incidence of "air rage" can be understood through the lens of inequality. Research on inequality typically examines the effects of relatively fixed, macrostructural forms of inequality, such as socioeconomic status; we examine how temporary exposure to both physical and situational inequality, induced by the design of environments, can foster antisocial behavior. We use a complete set of all onboard air rage incidents over several years from a large, international airline to test our predictions. Physical inequality on airplanes-that is, the presence of a first class cabin-is associated with more frequent air rage incidents in economy class. Situational inequality-boarding from the front (requiring walking through the first class cabin) versus the middle of the plane-also significantly increases the odds of air rage in both economy and first class. We show that physical design that highlights inequality can trigger antisocial behavior on airplanes. More broadly, these results point to the importance of considering the design of environments-from airplanes to office layouts to stadium seating-in understanding both the form and emergence of antisocial behavior.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".