Identification of Response Options to Artisanal and Small-Scale Gold Mining (ASGM) in Ghana via the Delphi Process
Bibliographic record
Abstract
The Delphi technique is a means of facilitating discussion among experts in order to develop consensus, and can be used for policy formulation. This article describes a modified Delphi approach in which 27 multi-disciplinary academics and 22 stakeholders from Ghana and North America were polled about ways to address negative effects of small-scale gold mining (ASGM) in Ghana. In early 2014, the academics, working in disciplinary groups, synthesized 17 response options based on data aggregated during an Integrated Assessment of ASGM in Ghana. The researchers participated in two rounds of Delphi polling in March and April 2014, during which 17 options were condensed into 12. Response options were rated via a 4-point Likert scale in terms of benefit (economic, environmental, and benefit to people) and feasibility (economic, social/cultural, political, and implementation). The six highest-scoring options populated a third Delphi poll, which 22 stakeholders from diverse sectors completed in April 2015. The academics and stakeholders also prioritized the response options using ranking exercises. The technique successfully gauged expert opinion on ASGM, and helped identify potential responses, policies and solutions for the sector. This is timely given that improvement to the ASGM sector is an important component within the UN Minamata Convention.
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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 | 0.113 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".