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Record W2992533760 · doi:10.3917/jibes.303.0135

Chapitre 8. Hommes ou robots dans l’espace. Approches éthique et juridique

2019· article· fr· W2992533760 on OpenAlexaff
Nathalie Nevejans

Bibliographic record

VenueJournal international de bioéthique et d'éthique des sciences · 2019
Typearticle
Languagefr
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsPrivy Council Office
Fundersnot available
KeywordsHumanityRobotSpace (punctuation)Human beingSociologyComputer sciencePolitical scienceArtificial intelligenceLaw

Abstract

fetched live from OpenAlex

Who of human or robot has its place in space? The robot, because it can replace human beings for exploration missions that are always particularly dangerous both for the health and the safety of astronauts. But human also tends to gain a place in space, when he can be assisted by the robot as a tool that facilitates his work, or when the machine can serve as a medium to extend humanity to the confines of the universe. All these hypotheses raise ethical and legal questions to which the article gives some solutions.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.006
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0230.004

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 head0.035
GPT teacher head0.317
Teacher spread0.282 · 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 designTheoretical or conceptual
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

Citations1
Published2019
Admission routes1
Has abstractyes

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