Team and communication impacts of remote work for complex aerospace system development
Bibliographic record
Abstract
Abstract Remote work is becoming increasingly common, a trend accelerated by the global COVID‐19 pandemic. Existing remote work research fails to address the challenges and needs of engineers working remotely in Complex Aerospace System Development (CASD), the field responsible for creating and operating aerospace systems. This article presents an exploratory study to understand the challenges, benefits, and strategies when working remotely in CASD. We interviewed 12 CASD engineers working remotely at a major aerospace corporation. We ground our findings in six characteristics of CASD work (complex systems; design paths and feedback loops; relationships with suppliers, customers and regulators; distinct knowledge and skills; one‐off innovation; and high cost of experimentation) and discuss how each of these characteristics challenges remote work. The findings show that CASD requires many teams to work together, and this is encouraged through informal communication, which almost disappears in a remote setting. CASD requires frequent feedback, and we found that feedback was slow when working remotely. Participants found it challenging to demonstrate systems to customers and verify drawings with suppliers, and the interpersonal relationships, which help to bridge disciplinary divides, were harder to maintain remotely. The one‐off nature of the systems designed meant that conceptual work was important, but participants lacked the virtual tools to do this effectively. Lastly, testing hardware components required close virtual communication between technicians and engineers, which was tricky in a detail‐oriented context. This study motivates areas for future work to better understand and address the nuances of remote work by engineers in CASD.
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.
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 | 0.014 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".