Extrapolation of Conflict Mitigation Strategies from Teams in Isolated Communities on Earth to Long Duration Space Exploration Missions
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-0399.vid With each passing minute, humans approach perfecting the technology that will allow for long-duration space exploration (LDSE) missions. Constant breakthroughs in scientific research mean that the goal of reaching the Moon, Mars, and beyond, is closer than ever before. Although the human race is bound to be technically equipped for LDSE missions, conflict mitigation will be a critical point on which the success of such missions hinges. As a result, this study seeks to answer questions on what differentiates conflict mitigation in the general case from conflict mitigation in ICE environments in Earth and space and how findings on successful conflict mitigation strategies can be extrapolated to ICE environments in space. From a general standpoint, in the realm of team dynamics, concepts revolving around relevant terminology, conflict types, conflict modes of resolution, individuality and team identity, the temporal evolution of conflict mitigation, and the McGrath Group Task Circumplex were discussed. Studying isolated communities on Earth, this paper considered projects like the Antarctic facility, NEEMO, HERA, Mars-520, and HI-SEAS to be examples of human collaboration in extreme environments. The transition to LDSE missions looked at experiments conducted on the International Space Station (ISS). By method of a meta-analysis combined with a Delphi analysis, a reasonable assessment of the framework for this investigation was achievable. Through the analysis of the Delphi study results, a definition of successful conflict mitigation comprising five factors was created, and the primary difference between conflict mitigation on Earth to ICE environments was identified. This difference being that conflict mitigation in ICE is critical while on Earth it is highly desired. The qualitative meta-analysis’s thematic results revealed that team characteristics, mitigation patterns and modes, and location were the most mentioned themes in the span of a total of 124 comments made in relation to the research questions. The results presented here are relevant and transferable to future LDSE missions to the Moon, Mars, and beyond.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".