Generative Learning and the Making of Ethical Space: Indigenizing Forest School Teacher Training in Wabanakik
Bibliographic record
Abstract
This reflection on community-driven research in process is written from the perspective of graduate student co-researchers collaborating with Wabanaki community co-researchers on a pilot project involving a Wabanaki and a non-Indigenous organization. Three Nations Education Group Inc. (TNEGI) represents three Wabanaki schools and communities in Northeast Turtle Island. The Child and Nature Alliance of Canada (CNAC) offers a Forest and Nature School Practitioner Course (FNSPC) for educators seeking to operate forest schools. These diverse organizations have developed a pilot FNSPC training for a group of TNEGI educators, with the purpose of Indigenizing the FNSPC. This is necessary to address the Eurocentric forest and nature school practices in Canada, which often fail to recognize the herstories, presence, rights, and diversity of Indigenous Peoples and places. TNEGI educators envision a land-based pedagogy that centers Wabanaki perspectives and merges Indigenous and Western knowledges. In the FNSPC pilot, the co-researchers generated course changes as they progressed through the pilot, decolonizing the content and format as they went. Developing this Indigenized version of the FNSPC will have far-reaching implications for the CNAC Forest School ethos and teacher training delivery. This essay maps our collaborative efforts thus far in creating an ethical research space within this Indigenous/non-Indigenous research initiative and lays out intentions for the road ahead.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.057 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| 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".