Exploring the cognitive structure of aircraft passengers' emotions in relation to their comfort experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it