The role of cross-cultural factors in long-duration international space missions: lessons from the SFINCSS-99 study.
Bibliographic record
Abstract
UNLABELLED: The role of cross-cultural factors in long-duration international space missions was examined during an isolation study that simulated many of the conditions aboard the International Space Station. METHODS: Interactions involving two heterogeneous crews and one homogeneous crew staying in isolation from 110 to 240 days were studied. Data consisted of post-isolation interviews with crewmembers, ground support personnel and management, observational data, and public statements by crewmembers. Data was analyzed using the techniques of linguistic anthropology and ethnography. RESULTS: Sub-cultural (organizational and professional) differences played a larger role than national differences in causing misunderstandings in this study. Conversely, some misunderstandings and conflicts were escalated by participants falsely assuming cultural differences or similarities. Comparison between the two heterogeneous crews showed the importance of training, personality factors, and commander and language skills in preventing and alleviating cultural misunderstandings. CONCLUSION: The study revealed a number of ways that cultural differences, real as well as assumed, can play a role and interact with other, non-cultural, factors in causing and/or precipitating conflict situations. It is postulated that such difficulties can be avoided by selecting culturally adaptive crewmembers and by cross-cultural and language training. Also the crew composition and role of commander were found to be important in mitigating conflict situations.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".