MétaCan
Menu
Back to cohort

Equitable utilization of the atmosphere: a rights-based approach to climate change?

2009· book-chapter· en· W4245331 on OpenAlexaboutno aff
Dinah Shelton

Bibliographic record

VenueCambridge University Press eBooks · 2009
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicClimate Change and Geoengineering
Canadian institutionsnot available
Fundersnot available
KeywordsAtmosphere (unit)Climate changeEnvironmental scienceAtmospheric sciencesNatural resource economicsMeteorologyGeographyEconomicsGeologyOceanography

Abstract

fetched live from OpenAlex

Most discussions of a rights-based approach to the environmental crises facing the planet quite appropriately centre on demanding that each state take action to prevent or mitigate environmental harm that diminishes, for those within its territory and jurisdiction, the enjoyment of internationally guaranteed human rights. Environmental degradation, in particular global climate change, undeniably has a negative impact on, and will increasingly limit, civil, political, economic, social and cultural rights, especially for the world's most vulnerable populations. Nonetheless, problems of standing, justiciability, ripeness and causality have been among the prominent problems encountered when individuals have sought to vindicate their rights through human rights litigation. Another rights-based approach is explored herein, whereby the government of a state may, and, indeed, arguably has the duty to, assert and defend the rights of its inhabitants, rather than remaining passive and ultimately defending itself for alleged rights-violating acts and omissions. The premise of the approach is that in the international community, which is organized on a territorial basis among some 192 independent, sovereign and juridically equal states, governments exist for the purpose of protecting the sovereign rights of the state and the human rights of their inhabitants, present and future, or, in constitutional language, ‘to ensure domestic tranquillity, provide for the common defence, promote the general welfare and ensure liberty to its citizens now and in the future’.

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)0.004
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.014
Scholarly communication0.0060.011
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.047
GPT teacher head0.208
Teacher spread0.161 · 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

Citations7
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueCambridge University Press eBooksSame topicClimate Change and GeoengineeringFrench-language works237,207