Knowing Home: Braiding Indigenous Science with Western Science, Book 1
Bibliographic record
Abstract
Knowing Home weaves Indigenous perspectives, worldviews, and wisdom practices into the science curriculum. It provides a window into the scientific knowledge and technological innovations of the Indigenous peoples who live in Northwestern North America, thus providing numerous examples and cases for developing science lessons and curricula. Knowing Home shows how Indigenous perspectives can give insight and guidance as we attempt to solve the complex environmental problems of the 21st century. Book 2 provides supportive research, case studies, curriculum projects and commentary that extends and enriches the chapters presented in Book 1. The chapters provide rich descriptions related to Indigenous cultural beliefs and values; teacher thinking about Indigenous Science; the perceptions and experiences of successful Indigenous students in secondary science; a metaphorical study of Indigenous students’ orientations (scientific, spiritual, utilitarian, aesthetic, and recreational) to the seashore and their adult orientations 19 years later; the use of digital video as a learning tool for secondary Indigenous students; a cross-cultural marine education program involving an exploration of Western Science and Indigenous Science related to the local Indigenous culture; and a WSÁNEĆ immersion school program focused on language revitalization and the concept of “knowledge of most worth.” This book is licensed under a CC-BY-NC-SA Creative Commons license, with the exception of some of the images. Please see front matter for more details about reuse.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".