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Record W3098466539 · doi:10.1089/ast.2019.2035

Team Processes and Outcomes During the AMADEE-18 Mars Analog Mission

2020· article· en· W3098466539 on OpenAlexaff
Julia McMenamin, Natalie J. Allen, M. Battler

Bibliographic record

VenueAstrobiology · 2020
Typearticle
Languageen
FieldPsychology
TopicTeam Dynamics and Performance
Canadian institutionsShared Services CanadaWestern University
Fundersnot available
KeywordsSocial loafingTeamworkApplied psychologyPsychologyContext (archaeology)FidelityMars Exploration ProgramDemographicsSocial psychologyComputer scienceManagementGeography

Abstract

fetched live from OpenAlex

The aim of this study was to examine team functioning within the context of the AMADEE 18 Mars analog project, which took place in Oman in the winter of 2018. Five "Analog Astronauts" participated in this study. Each completed measures of individual-level variables, including demographics and personality, before the simulated Mars mission began. At several time points during the mission, and once at the end, participants completed measures of individual stress reactions, and teamwork-related variables, including several types of team conflict, citizenship behavior, in-role behavior, counterproductive behavior, and social loafing. Each participant also reported how well he or she felt the team performed. The results indicate an overall positive, successful teamwork experience. Factors including measurement issues, psychological simulation fidelity, and qualities of the team likely influenced these results. Measuring important team- and individual-level variables during additional space analog events, while considering factors related to psychological fidelity, will allow for the compilation of data to better understand the factors affecting teams in these unusual contexts.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.485

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.290
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

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