Fataluku medicinal ethnobotany and the East Timorese military resistance
Bibliographic record
Abstract
BACKGROUND: An ethnobotanical study of medicinal and poisonous plants used by the East Timor resistance was undertaken in the Lautem District of East Timor to study medicinal plant use in the region. Interviews were conducted with a single key consultant from the resistance army who belonged to the Fataluku culture. This study is of importance as a historical document and because no previous medicinal ethnobotanical studies on this region exist. METHODS: A rapid ethnobotanical survey of medicinal and poisonous plants was conducted through the proposed Conis Santana National Park in the Lautem district of East Timor. Medicinal and poisonous plants were identified by a Consultant and data was collected by the authors using classical descriptive ethnobotanical techniques (i.e. no quantitative measures) through an unstructured open ended interview. RESULTS: During the survey 40 medicinal and poisonous plants were identified by the Consultant and collected by the authors. Defining characteristics of the Consultant's knowledge include a high frequency use of trees, heavily forested habitats, leaves, decoctions and drinks for a range of conditions relevant to a resistance army. CONCLUSION: Despite limitations of the study, important contributions of this study include preservation of a part of the cultural history of the resistance movement and traditional botanical knowledge of the Fataluku. Furthermore, initial findings may indicate that traditional botanical knowledge is unique amongst different East Timorese cultures in terms of plant selection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".