Connecting children to nature through the integration of Indigenous Ecological Knowledge into Early Childhood Environmental Education
Bibliographic record
Abstract
Abstract In this paper, we draw on the ontology and epistemology of the local Kasena ethnic group in Northern Ghana to explore Early Childhood Environmental Education. The study, taking place in Boania Primary School, drew on the concept of two-eyed seeing, where both western and Indigenous epistemologies and ontologies were taught. In this way, Indigenous Ecological Knowledge was integrated into the Early Childhood Environmental Education programme for the Kindergarten two classroom environmental studies topics. Two Indigenous Elders led the integration of local knowledge into environmental studies topics by visiting the school to teach the children through taking them outdoors for learning activities. After this, in-depth interviews were held with the teacher, Indigenous Elders, and nine children regarding their experiences. The purpose of the study was to explore how Indigenous Ecological Knowledges can help instil in children positive environmental attitudes and values, while also connecting them to nature and offering them a more relational understanding of human to nature relationships. Based on the Indigenous cultural framework of respect, reciprocity, and responsibility towards nature, the findings show that the integration of Indigenous Ecological Knowledge into environmental education has the potential to improve our relationships with the environment.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".