The Importance of Context: Assessing the Benefits and Limitations of Participatory Mapping for Empowering Indigenous Communities in the Comarca Ngäbe-Buglé, Panama
Bibliographic record
Abstract
Indigenous communities have been involved in participatory mapping projects to protect their territories and manage their resources for decades. However, while tremendous advances have been achieved in many settings, the use of maps by indigenous peoples is very uneven. Here we present the case of a team of university researchers, indigenous students, and local investigators who used a participatory approach to map cultural landscapes and mature forest cover in the Comarca Ngäbe-Buglé of Panama. This article examines the success and limitations of efforts to empower indigenous people in the region to use mapping tools for conservation and resource management. The project, while it provides a useful example of how to build a participatory research team to produce maps that better reflect indigenous points of view, fell short of empowering indigenous authorities to use geographic tools to manage their territories. This is due mainly to the lack of administrative capacity needed to make use of geospatial information. We argue that cartographers involved in participatory projects, while typically attentive to the problems of marginalization, need to pay more attention to the broader socioeconomic contexts of their work and to redouble their efforts to respond to the challenges of the digital divide, which is a symptom of broader socioeconomic and political inequalities stemming from the legacies of colonialism.
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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 | 0.091 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| 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".