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Record W1903260915 · doi:10.1163/18786561-00504003

State Responsibility for Environmental Harm from Climate Engineering

2015· article· en· W1903260915 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueClimate Law · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHarmAccountabilityState responsibilityGlobal warmingClimate changeContext (archaeology)International lawPolitical scienceEnvironmental resource managementIntervention (counseling)Environmental planningBusinessEnvironmental scienceGeographyLawPsychologyEcology

Abstract

fetched live from OpenAlex

Some have proposed that climate-engineering methods could be developed to offset climate change. However, whilst some of these methods, in particular a form of solar-radiation management referred to as stratospheric aerosol injection ( sai ), could potentially reduce the overall degree of global warming as well as some associated risks, they are also likely to redistribute some environmental risks globally. Moreover, they could give rise to new risks, raising the issue of legal responsibility for transboundary harm caused. This article examines the question of international accountability of states for an increased risk of environmental harm arising from a large-scale climate intervention using sai , and the legal consequences that would follow. Examination of the applicability of customary rules on state responsibility to sai are useful for understanding the limitations of the existing accountability framework for climate engineering, particularly in the context of global environmental problems involving risk-risk trade-offs and large uncertainties.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.001

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 head0.029
GPT teacher head0.241
Teacher spread0.212 · 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