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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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 source (direct Gemma or distilled Codex), 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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