Policy Enabling Environment of Mining Sector in Tanzania: A Review of Opportunities and Challenges
Bibliographic record
Abstract
Mining has increasingly become an important contributor to the economy of developing countries including Tanzania. Since independence, Tanzania has made several efforts in response to address the challenges in the mining sector to enhance its contribution to the national economy. However, such efforts have not been successful in addressing the persisting challenges, which includes lack of expected benefits, failure to develop policy options for making the investment environment supportive for all actors in the sector; hence failure to use mineral wealth sustainably. The reasons for the persistence of these challenges are not well documented especially in relation to the policy framework. While some scholars attribute these challenges to bad deals with mining companies, others blame the government for its failure to effectively implement, monitor and enforce the existing regulatory framework. This paper reviews the policy enabling environment of the mining sector in Tanzania. The results indicate that Tanzania has taken measures to create some opportunities through policy enabling environment. However, the measures have not been able to achieve the expected results due to the persistence of targeted challenges in the sector. In view of this, improvement of the policy framework is particularly needed where policy gaps have accelerated for poor practices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".