Maliqua: A study within Malakit, a project on malaria and gold miners in French Guiana
Bibliographic record
Abstract
Summary Malaria is endemic in French Guiana, especially within the gold mining community working illegally. Gold miners travel to remote camps in the forest to carry out their activities, exposing themselves to the presumed contamination area. This paper presents the results of a qualitative case study of the Malakit project, a free distribution of self-diagnosis and self-treatment kits, along with appropriate training/information from health facilitators, at resting sites in Brazil and Suriname on the borders with French Guiana. This study documents how Malakit is part of the care trajectory of gold miners. The data was collected using three methods: 1) on-site observation; 2) semi-structured individual interviews (n=26); 3) semi-structured group interviews (n=2). The results inform us that Malakit responds to the need for treatment and facilitates access to care. Gold miners say they trust the facilitators and receive accurate explanations. The majority of participants find the kit easy to use and to carry and explanations given were sufficient, although some people needed to be reminded how to use it once in the forest. Results remind us that malaria among illegal gold miners in French Guiana is a question of social inequalities in health, where the interaction of the health, social, economic and political contexts of Brazil and French Guiana influence exposure to numerous risk factors. Thus, malaria intervention practices such as Malakit cannot be carried out without considering the complexity generated by social inequalities in health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".