I will use the left hand in school and the right hand at home: A two-eyed seeing approach
Bibliographic record
Abstract
Globally, there are increasing demands to decolonize education. As a result, the integration of Indigenous Knowledges and worldviews into Early Childhood Education has become a pertinent issue. Few studies have examined methodological frameworks for integrating Indigenous Knowledges into early learning in Ghana. This article examined the integration of Indigenous Knowledge into Early Childhood Education at a rural primary school. A two-eyed seeing Indigenous methodology was employed to integrate the local Kasena Indigenous Knowledge into a Kindergarten 2 classroom environmental studies topics. The ages of the children ranged from 6-8 years. As the holders of Indigenous knowledge, two Kasena Indigenous Elders helped to integrate Indigenous Knowledge into topics by visiting the school to teach and take children out on outdoor learning activities. After this, in-depth interviews were held with research participants. This paper focuses specifically on the methodology employed and highlights some of the outcomes. The study found that adopting a two-eyed seeing approach: challenged Western Knowledge’s dominance over Indigenous Knowledge in early learning; provided a framework to guide practice for integrating Indigenous Knowledge; and created awareness of the existence of an Indigenous worldview.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".