Earth worlds and Indigenous dream-making: A reflection on teaching for beauty, repair, and balance
Bibliographic record
Abstract
Based on long-term educational relationships with Indigenous communities in Peru, the U.S., and Canada, this article reflects on place-based Indigenous education in and out-of-school. From small community-based schools to tribal tertiary learning spaces, Indigenous educational leaders counter schooling as an instrument of coloniality/modernity by centering their knowledges toward relationships of interdependence for good human and planetary living. Confronting ontic and epistemic threats, Indigenous educators and students energize educational design and practice. I propose these processes as Indigenous dream-making, the daily work of honoring the beauty of Native earth worlds, repairing harms to the earth and her beings, and balancing difficult realities with good living. In this time of increased attention to sustainability in education and climate change action, bold Indigenous educational processes challenge human communities to learn in and across earth worlds and to teach for compassionate interconnection that can supplant the course of destructive relentless development.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.037 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".