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Record W4367307375 · doi:10.33002/jelp03.01.02

Climate Change Regulations of Corporations in Tanzania: A Case for Dilute Interventionism and Veto Firewall Paradigm

2023· article· en· W4367307375 on OpenAlexvenueno aff
Kikelomo Oluwaseun Kila

Bibliographic record

VenueJournal of Environmental Law & Policy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
Fundersnot available
KeywordsInterventionism (politics)Climate changeVetoTanzaniaBusinessClimate change mitigationMethanosaetaEconomicsNatural resource economicsPolitical scienceLawEcologySocioeconomicsInternational relations

Abstract

fetched live from OpenAlex

Corporations operating in developing countries generally adopt an obstructionist approach to climate change and environmental regulation, particularly in states with weaker economic bargaining strength. Tanzania is one of the African states suffering the disproportionate impacts of climate change but with a weak regulatory capacity to restrain adverse corporate climate change impacting activities. This article critically analyses the climate change regulatory framework of corporations in Tanzania and proposes the implementation of the Dilute Interventionism Model as an innovative solution for regulating corporate activities in climate change mitigation in the country. The model combines prescriptive and facilitative measures in regulating corporations to mitigate the effects of climate change. The article also identifies the need for Veto Firewall protection to safeguard the independence of the sole independent regulator established to regulate the climate change activities of corporations in Tanzania. This article adopts the Dilute Interventionism Pyramid which depicts the steps required to implement the Dilute Interventionism Model in Tanzania. The challenges to the implementation of the Dilute Interventionism and Veto Firewall Paradigm in Tanzania are also discussed, including resistance from corporations, inadequate funding, and lack of technical capacity and the potential solutions to these challenges are briefly highlighted.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
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)0.0000.000

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 head0.126
GPT teacher head0.353
Teacher spread0.227 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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