Individual knowledge acquisition by teachers to promote the ecological value of sacred Kaya forests in southern Kenya
Bibliographic record
Abstract
Demographic pressure is leading to an ever-increasing demand for natural resources. In large parts of Sub-Saharan Africa, natural ecosystems have been transformed into agricultural land, pastures, plantations, or settlement areas. The last remnants of natural ecosystems are preserved for biological and cultural reasons. The Mijikenda Kaya forests in coastal Kenya are small forest remnants with high biological and cultural value. Given demographic pressure and a lack of awareness of the value of bio- and cultural diversity, the destruction of these forest habitats is alarming. Environmental education in schools may help to increase awareness for the need to preserve these ecosystems. In this study, we interviewed teachers from schools located around Kaya Kambe forest in coastal Kenya. We used a standardized questionnaire to analyze the degree of awareness through environmental education, teachers´ attitudes to environmental issues in general, and particularly to sacred Kaya forests. We found that environmental education is of low priority for teachers in schools assessed. One third of respondents do not incorporate environmental topics in their teaching at all. Teachers who have already had environmental education themselves and therefore have respective knowledge are prone to incorporate these topics in their lessons. However, there is hardly any reference to the conservation of Kaya forests for their spiritual heritage. Only teachers who have a relation with Kaya forests and the local culture considered the forest to be of high biological and cultural relevance and worth conserving. Our study shows that environmental education is still under-represented in schools in Kenya, and should be given a higher priority in the school curricula in order to increase awareness of the need to preserve biological and cultural diversity as well as natural resources for the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".