Mapping the vicious cycles of community gold mining (CGM): a case study of the CGM sites at Sukabumi Regency, Indonesia
Bibliographic record
Abstract
Abstract Community Gold Mining (CGM) in Indonesia faces significant challenges, with a specific concern being the use of mercury. Mercury is a highly toxic chemical commonly utilized in the Trommel Mercury (TM) gold extraction method, known locally as the Glundung method. Although the government has initiated programs to reduce mercury usage, such as encouraging researchers to develop non-mercury gold extraction methods, progress has been slow, and the impact has been limited. The growth of new CGM sites is outpacing these efforts, leading to an increased use of mercury and unmanageable chemical risks. Previous research has identified a vicious cycle within the CGM sector. However, no existing model illustrates this cycle. This study seeks to map the scope of CGM at its essential stages and translate them into variables to create a causal and basic model. However, Sukabumi Regency in Indonesia hosts numerous CGM sites, and a case study was conducted in the Simpenan Sub-District between 2018 and 2020. A recent site visit in August 2023 revealed continued growth in CGM site numbers within the broader area. This growth corresponds to an increase in mercury released into the environment, which poses a growing threat to public health. The study employed ArcGIS and Powersim 10 System Dynamics Software, utilizing data collected through observations, investigative methods, and reference studies. The results include two significant contributions: first, a model of current CGM activities in the form of a Causal Loops Diagram (CLD) called “the Turtle Map CLD Model of the CGM”. Second, a model depicting “the vicious cycle of CGM” highlights problematic stages within CGM. Both models represent the current state of CGM in Indonesia, showcasing the existence of vicious cycles in ongoing CGM sites. These models can guide future efforts to identify progressive solutions, especially in support of programs aimed at reducing mercury usage.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".