Traditional Communities and Mental Maps: Dialogues between Local Knowledge and Cartography from the Socioenvironmental Atlas of Lençóis Maranhenses, Brazil
Bibliographic record
Abstract
The Lençóis Maranhenses region, located in the state of Maranhão in northeastern Brazil, constitutes an area that includes a national park and presents extreme physical, geographic and climatic contrasts in addition to economic diversity and emerging tourism. Scattered throughout this portion of the Brazilian territory are local inhabitants whose traditional lifestyles are characterized by agricultural, extractive, fishing and animal husbandry activities. These local residents use guidance systems and mental maps developed through their long history, interaction with nature, and knowledge of the environment in which they live and work. Based on sketches prepared by residents and by Health Agents serving the communities, and with the support of cartographic-based materials produced by the team of the Socioenvironmental Atlas of Lençóis Maranhenses (ASALM, Portuguese abbreviation for Socioenvironmental Atlas of Lençóis Maranhenses), we present a set of digital and interactive cartographic materials that reproduce the movements, uses and practices of the families of these communities as well as the environmental dynamics of this vast region. Such cartography can serve as an instrument of planning, understanding and action, both to safeguard the rights of the local residents and for the handling and management of natural resources. Based on the dialogue between local knowledge and cartography, we present the methods, processes and results of our research project.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".