Climate Change Regulations of Corporations in Tanzania: A Case for Dilute Interventionism and Veto Firewall Paradigm
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".