Exploring the cognitive structure of aircraft passengers' emotions in relation to their comfort experience
Bibliographic record
Abstract
Emotion descriptions were elicited from participants' written accounts of their comfort experience and grouped according to the emotion model by Ortony, Clore, and Collins (OCC). The cognitive structure and specific appraisal patterns of passengers were explored on three levels of passenger's concerns (goals, standards, and aspects), their focus during the flight (including the mediating cabin elements) and the resulting emotions. Four emotion groups were highlighted as relevant to flight comfort. Wellbeing (e.g., joy, distress) emotions were the most frequently mentioned group by participants when focused on the consequences of interaction with cabin features such as seat, IFE and service, pertaining to participants' personal goals (e.g., security, calmness). The cognitive underpinning of prospect-based (e.g., satisfied) emotions included similar goals except that participants evaluated the consequences of their interaction with the seat, legroom, IFE and service relevant to their expectations and anticipations. The emotions in wellbeing/attribution compound group were elicited upon evaluating the consequences of the actions of agents (e.g., service, neighbors). Thus emotions anger and gratitude emerged when those actions yielded pleasing or unpleasing consequences for participants. Attraction (e.g., liking) emotions were generated once passengers developed liking or disliking for certain aspects (e.g., aesthetics, physical fitting) of the seat and legroom. Subsequently, a model of cognitive structure of passengers' emotions was constructed for the flight context highlighting the seat and services as the central (most frequently regarded) features to passengers' emotional experiences. The proposed model enables designers to recognize the types of experiences that should be delivered to ensure that passengers feel comfortable.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".