Local and indigenous knowledge (LIK) in science learning: A systematic literature review
Bibliographic record
Abstract
This research aimed to analyse the literature regarding Local and Indigenous Knowledge (LIK) in science teaching and learning. This research uses a Systematic Literature Review (SLR) to identify articles focusing on studies regarding LIK in science education. This research explores 52 articles from Scopus and Web of Science published between 2014 and 2023 from various countries. The SLR results show that the number of publications increased yearly. LIK is a recognised research topic in various countries, such as Indonesia, the United States, Canada, Australia, and African countries. The SLR results also show types of LIK consisting of daily lifestyle behaviour, system development in society, and knowledge and practice of investigation by the community. These types related to issues in science issue of climate, ecology, medicinal plants, and astronomy. These issues are studied from the perspective of indigenous knowledge, which is harmonised with modern scientific knowledge. LIK implementation strategies in science learning include community-based and place-based education learning development strategies. Implementation of different strategies is the development of a formal curriculum that accommodates LIK, such as Cross-Curriculum Cultural Priorities, Integration of medicinal plants as important content in K-12 curriculum subjects in the USA, Development of chemistry and physics practicums based on knowledge of indigenous communities and culture, and curriculum development in Traditional Ecological Knowledge (TEK).
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".