Correlation Status of Cultural Significance Index to Characteristics of Krui Indigenous People as a Base for Repong Damar Conservation Efforts
Bibliographic record
Abstract
One of the provinces in Indonesia that has local wisdom in forest management is Lampung Province, specifically in Pesisir Barat District.Lampung Province is dominated by indigenous Lampung tribes.The local wisdom they practice is related to the knowledge and utilization of vegetation around the Repong Damar.This study aims to examine vegetation conservation efforts based on cultural values in relation to the cultural characteristics of Lampung tribal communities.The research approach uses quantitative methods and data collection based on snowball sampling techniques.Data analysis used included: Index of Cultural Significance (ICS) formula in analyzing the importance of vegetation; and Spearman Rank in analyzing the correlation of indigenous characteristics to ICS.The study showed that of the 29 species found, ginger (Zingiber officinale Rosc.) is the vegetation that has the highest ICS value of 42.The Shorea javanica has ICS value as 22.5 while the species of jengkol (Archidendron pauciflorum Benth.), kabau (Archidendron bubalinum Jack.), bayur (Pterospermum javanicum Jungh.), pepper (Piper nigrum L.), suren (Toona sureni Merr.), taro (Colocasia esculenta Schott.),mangosteen (Garcinia mangostana L.), melinjo (Gnetum gnemon L.), jackfruit (Artocarpus heterophyllus Lam.), petai (Parkia speciosa Hassk.), tupak/kemundung (Baccaurea racemosa Muell.), and coffee (Coffea sp.) have the lowest ICS, the each value is 4. The ICS shows that indigenous people mostly used ginger as medicinal plants in their daily lives.Age (> 45 years) and duration of stay (indigenous people) have a positive correlation to ICS, while education level, age (< 45 years), education and duration of stay (migrants) have a negative correlation to ICS.Based on the results of this analysis, education has no correlation to ICS therefore the younger generation (< 45 years) and migrants need various extension education and or various training to increase skills in managing the Krui damar forest because their condition has a negative correlation with the cultural index.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".