Unintended Consequences of Tax Tyranny on Mineral, Oil and Gas Companies Hampering Social Stability and Growth. These are also making the MOGI and SAMOG Companies more Conservative, and that is not Good News for the Government, Industry, or Employees… Actually for None!
Bibliographic record
Abstract
Governments often impose resource rent taxes or windfall taxes on extractive industries to capture a share of the profits from non-renewable natural resources. Since minerals, oil, and gas are finite and belong to the state, these industries often pay higher royalties, extraction taxes, and profit-based levies. Mining, oil, and gas sectors in these countries have generally faced higher effective tax rates compared to the manufacturing industry, primarily due to additional levies like royalties, resource rent taxes, and windfall taxes. These measures aim to ensure that nations receive a fair share of revenues from their natural resources while balancing the need to attract and retain industry investment. Analyzing the tax regimes for the mining, oil, and gas sectors compared to the manufacturing industry over the past 15 years across countries like India, Australia, Canada, the UK, China, Brazil, and South Africa reveals distinct fiscal approaches. Relatively high tax rates in the Mining, Oil & Gas sectors can have several unintended negative consequences. The influence of militias and small-time politicians in mining has been growing, especially in Africa, Latin America, and parts of Asia. This trend is driven by weak governance, high-value mineral resources, and political instability.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".