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Record W2124063117 · doi:10.1017/s0020589309001213

CLIMATE FINANCE AND ITS GOVERNANCE: MOVING TO A LOW CARBON ECONOMY THROUGH SOCIALLY RESPONSIBLE FINANCING?

2009· article· en· W2124063117 on OpenAlexaff
Benjamin J. Richardson

Bibliographic record

VenueInternational and Comparative Law Quarterly · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsYork University
Fundersnot available
KeywordsClimate FinanceCorporate governanceEmissions tradingGreenhouse gasClimate changeBusinessLeverage (statistics)Abandonment (legal)FinanceEconomic policyEconomicsPolitical scienceEconomic growthDeveloping country

Abstract

fetched live from OpenAlex

Abstract ‘Climate finance’ is becoming an important feature of the emerging legal and policy regimes to address global warming. However, the current approach largely confines the financial sector to a transactional agent to mobilise capital for clean energy and to broker emission allowance trading. The sector's potential to leverage more sweeping positive changes in the economy as sought historically through the movement for socially responsible investment (SRI) has been insufficiently acknowledged. Indirectly, by regulating greenhouse gases the legal system is helping to create a business case for investors to respond to climate change threats. However, the potential contribution of SRI to address climate change problems more comprehensively is presently limited owing to inadequate governance frameworks, as well the sector's increasing abandonment of its traditional ethical agenda.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.011
Scholarly communication0.0100.006
Open science0.0010.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.044
GPT teacher head0.280
Teacher spread0.236 · 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

Citations8
Published2009
Admission routes1
Has abstractyes

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