Indigenisation of conservation education in New Zealand
Bibliographic record
Abstract
Indigenous Maori youth struggle to connect with science delivered in a Eurocentric model of education in Aotearoa, New Zealand. In transforming conservation biology through Indigenous perspectives, we asked whether Maori knowledge-based resources and traditional schooling (wananga) methodologies increased the connection of Maori youth (rangatahi) to conservation science. We collaborated with a Maori environmental science body to run a culturally based environmental program (noho taiao) attended by 70 youth from three Maori-centric schools. We undertook surveys to assess baseline scientific understanding and to gauge how their understanding of the Maori-based conservation principles we introduced shifted over the course of the program. We developed a bilingual gaming app to introduce basic environmental concepts from both cultural perspectives, measuring its impact on knowledge retention for these students, and others at a Eurocentric school. Indigenous contexts for conservation learning markedly increased uptake of knowledge content, and enthusiasm for conservation concepts. After the program, Maori students reported that science was more accessible and relevant. Gaming as an educational medium was successful in engaging youth generally, but students primed by experiential learning from Indigenous perspectives had increased knowledge gained. Enabling rangatahi to explore place-based learning within a relevant cultural context allowed them to understand their duty of care to the environment (te taiao). Utilising Maori engagement mediums and mentors that resonate with youth are key to encouraging more Maori youth into conservation science. Therefore, empowering youth to draw from Indigenous ways of knowing, being and doing can create a step-change in science participation and leadership.
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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 | 0.002 | 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 | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".