Indigenous Children’s Knowledge About Non-timber Forest Products in Suriname
Bibliographic record
Abstract
Childhood and adolescence are important life stages for the acquisition of knowledge about non-timber forest products (NTFPs). We show at which stage in life traditional plant knowledge is learned and analyze whether cross-cultural ethnobotanical knowledge transmission takes place. We evaluate whether the degree of forest dependency influences ethnobotanical knowledge by comparing two indigenous communities in Suriname. Traditional knowledge was documented and vouchers collected during forest walks with adult informants. Questionnaires were completed by 74 schoolchildren (age 4 to 14) to capture their knowledge of names and uses of nine important NTFPs. We tested for knowledge differences by ethnicity and NTFP categories. Local names for NTFPs were analyzed to determine cross-cultural transmission of ethnobotanical knowledge. Children from the forest-dependent Trio community ( n = 23) possessed similar knowledge of NTFPs as their more urbanized peers from Apoera ( n = 51). NTFP uses were acquired at an earlier age than plant names. Food and commercial NTFP uses were better known than medicinal plant uses. Cross-cultural transfer of knowledge occurred between the two communities. NTFP knowledge of children appeared to be influenced more by the time they spent within the forest, either walking to school or walking to agricultural plots, than by the level of forest dependency or acculturation.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".