Investing to Empower or Deteriorate? A Critical Assessment of the Dialectical Relationship Between Poverty and Mineral Mining in Ghana
Bibliographic record
Abstract
Conventional knowledge would suggest that the richer a country is in its natural resource, the better off it will be economically. The common prose these days is that countries will do better if they integrate into the world economy through trade liberalization, privation, deregulation, and a general openness to foreign investments. This idea hinges benefits such as improved output, better living standards for the populace, and overall economic growth. However, the growing trend of economic globalization (in this context transnational mining investments) is met with many complexities. A major aspect of the paradox resides in the relationship between profit-making and sustainable development. Can a company that seeks to maximise its returns be trusted to be an agent of development? What are the ramifications of mining – economically, socially, politically and environmentally? How are lives impacted by mining? Evidence from mineral-rich countries, especially in the global South, show that the most endowed countries are in some kind of a 'curse'. Be it a result of conflict, misappropriation, rent-seeking or the lack of social responsibility on the part of companies, these countries are among some of the poorest in the world. This paper seeks to evaluate the dialectical relationship between poverty and mining by arguing for proper 'safety nets' that will decrease levels of poverty, inequality and social injustice, and also make companies operate in a socially and environmentally responsible manner.
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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 | 0.002 | 0.003 |
| 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 | 0.009 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".