Questioning the Status Quo: Can Stakeholder Participation Improve Implementation of Small-Scale Mining Laws in Ghana?
Bibliographic record
Abstract
Ghana’s small-scale mining sector faces complex challenges, including environmental degradation and pollution, loss of life and increased health risks, despite several years of implementation of small-scale mining laws. These challenges, generally, are known to have escalated because of illegal small-scale mining, locally known as “galamsey”. Despite the illegal status of this category of miners, this paper examines the extent to which stakeholder participation can improve implementation of mining regulations and also address the marginalization of these miners. This paper about stakeholder participation is timely because news reports in mid-2016 mentioned that the Government of Ghana, despite many years of disengagement, is now planning to engage with galamsey operators, in terms of registration, as part of measures to effectively regulate the activities of small-scale miners. Findings from fieldwork indicate that (1) chiefs are seldom consulted in the granting of mining licenses; (2) illegal miners do not participate in the implementation of small-scale mining laws; and (3) stakeholders, such as officers in district mining offices, feel distant from the implementation process. Against the backdrop of these findings, it remains useful to explore the extent to which effective stakeholder participation could help overcome the status quo—particularly its ramifications for both the implementation of ASM laws and the eradication of other underlying challenges the sector faces.
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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 | 0.047 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".