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Record W4324149214 · doi:10.5539/ilr.v12n1p87

Application of the Principle of Distinction under IHL in Outer Space

2023· article· en· W4324149214 on OpenAlexvenueno aff
Wenjun Yan, Haoyu Cui

Bibliographic record

VenueInternational Law Research · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsnot available
FundersOffice of Defense ProgramsU.S. Department of Defense
KeywordsOuter spaceSpace (punctuation)Human spaceflightComputer securityOrder (exchange)LawComputer sciencePolitical scienceSpace explorationEngineeringAerospace engineeringBusiness

Abstract

fetched live from OpenAlex

The principle of distinction can be applied to armed conflicts in outer space. Under the principle of distinction, combatants refer to armed personnel who directly participate in hostile actions in outer space, and military objects refer to those that have made actual contributions to military operations in outer space and can provide a definite military interest. All other personnel and objects are non-military targets that must not be attacked. Objects on which spaceflight participants depend for their survival, certain areas and cultural relics on celestial bodies, outer space, the natural environment of the earth, and space objects used for humanitarian relief are special protected objects. In order to implement the principle of distinction, the defender needs to effectively differentiate its own personnel and objects, and the attacker also needs to take feasible preventive measures.

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)0.011
metaresearch head score (Gemma)0.012
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 score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0070.036
Scholarly communication0.0070.009
Open science0.0020.008
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0060.002

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 head0.062
GPT teacher head0.405
Teacher spread0.343 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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