Climate Crisis and Indigenous Youth Resilience
Bibliographic record
Abstract
As a way to respond to the global state of human disconnect from the environment, AIR (the Alliance for Intergenerational Resilience) and collaborators initiated two virtual Wisdom Councils between Indigenous Elders, Knowledge Keepers, and youth to explore how to centre Indigenous thinking and life ways into Indigenous and intercultural planetary healing work. Wisdom Council participants represented Indigenous peoples from Aotearoa/New Zealand (Ngāi Te Rangi, Ngāi Te Ranginui, Te Arawa & Ngāti Porou iwi, Tauranga Moana; Waitaha taiwhenua ki Waitaki); Turtle Island/Canada (Coast Salish, Nêhiyaw/Cree Nation, Dene, Tsimishan & Scottish, Cree and Metis, Mi’kmaq/ Vancouver Island, Anishinaabe, and Deshkhan Ziibi/SW Ontario); and Alba/Scotland (Outer Hebrides and Shetland). A key theme present in the dialogue was the importance of Indigenous languages for informing our conduct as human beings as these languages are deeply rooted in the environment. Significantly, there is a connection found when considering the health of Indigenous cultures and languages and the health of the planet. In addition to this, we found that there is a need to strengthen connections between Elders and youth, not only for the purposes of intergenerational knowledge transmission but to also deepen mutual understandings regarding processes and protocol to guide the adaptation of Indigenous knowledges and lifeways to meet contemporary challenges. In order to deepen the conversations held in these Wisdom Councils, we propose to hold a talking circle for Indigenous youth with the purpose of exploring challenges faced when it comes to carrying traditional knowledge forward in contemporary times.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".