MétaCan
Menu
Back to cohort
Record W2082735258 · doi:10.3390/su5010316

The Rule of Ecological Law: The Legal Complement to Degrowth Economics

2013· article· en· W2082735258 on OpenAlexaff
Geoffrey Garver

Bibliographic record

VenueSustainability · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsMcGill University
Fundersnot available
KeywordsDegrowthPlanetary boundariesEnvironmental lawSubsidiarityDoctrineRule of lawEcological economicsLawEcological crisisEcologyEconomicsEconomic systemPolitical scienceEuropean unionSustainable developmentBiologyInternational tradeSustainability

Abstract

fetched live from OpenAlex

The rule of ecological law is a fitting complement to degrowth. Planetary boundaries of safe operating space for humanity, along with complementary measures and principles, provide scientific and ethical foundations of the rule of ecological law, which should have several reinforcing features. First, it should recognize humans are part of Earth’s life systems. Second, ecological limits must have primacy over social and economic regimes. Third, the rule of ecological law must permeate all areas of law. Fourth, it should focus on radically reducing material and energy throughput. Fifth, it must be global, but distributed, using the principle of subsidiarity. Sixth, it must ensure fair sharing of resources among present and future generations of humans and other life. Seventh, it must be binding and supranational, with supremacy over sub-global legal regimes as necessary. Eighth, it requires a greatly expanded program of research and monitoring. Ninth, it requires precaution about crossing global ecological boundaries. Tenth, it must be adaptive. Although the transition from a growth-insistent economy headed toward ecological collapse to an economy based on the rule of ecological law is elusive, the European Union may be a useful structural model.

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 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 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: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.999

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.261
Teacher spread0.213 · 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 teacher head, 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

Citations41
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicClimate Change Policy and EconomicsFrench-language works237,207