The Study and Design of Collaboration Tools for Flight Attendants
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
Collaboration is a core component of work activities amongst flight attendants. This is as they work to promote onboard safety and deliver a high level of customer service. Yet we know little of how flight attendants collaborate and how we can best design technology to support this collaboration. Through an interview study with flight attendants, the authors explored their collaborative practices and processes and how technology aided such practices. While technologies like interphones and flight attendant call buttons act as collaboration tools, they identified instances where the usability and functionality of these devices were barriers for maintaining efficient communication, situation awareness, and information exchange. The authors used these results to identify design suggestions for technology that can enhance communication and collaboration in aircraft settings amongst flight attendants. To illustrate these design suggestions, they designed and developed Smart Crew, a smartwatch application that allows flight attendants to maintain an awareness of each other and communicate through messaging with haptic feedback. Smart Crew is designed with an emphasis on real time information access, location updates and direct communication between flight attendants regardless of their location on the airplane.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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