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Record W3133983388 · doi:10.1163/15718123-bja10046

India’s Anti-Satellite Test: from the Perspective of International Space Law and the Law of Armed Conflict

2021· article· en· W3133983388 on OpenAlexaff
Shakeel Ahmad

Bibliographic record

VenueInternational Criminal Law Review · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsMcGill University
Fundersnot available
KeywordsOuter spaceLawDisarmamentSpace lawInternational lawSpace (punctuation)Political scienceArms controlDeterrence theoryInternational humanitarian lawSpace debrisMilitarizationLaw and economicsSociologyEngineeringComputer sciencePoliticsSpacecraft

Abstract

fetched live from OpenAlex

Abstract To enhance their strategic position, some spacefaring States are engaged in exploiting legal lacunae of international space treaties. Consequently, there is an increase of militarization of outer space. As an instance of such activities, an anti-satellite ( asat ) test by India represents a strategic move to enhance its deterrence capability rather than earnestly adhering to international space law. Such actions can potentially increase the element of uncertainty in international law, particularly the international space law. The pursuit of military strategic interests in space has increased the possibility of an arms race in space. This article argues that asat tests not only violate certain principles of international law but also undermine the efforts for arms control and disarmament in the outer space. In this regard, an effective role of the international community is required to curb the arms race imperative for a safe and sustainable outer space environment.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0060.025
Scholarly communication0.0160.004
Open science0.0020.004
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.306
Teacher spread0.279 · 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 designNot applicable
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

Citations3
Published2021
Admission routes1
Has abstractyes

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