Restoring Relationships: Indigenous Ways of Knowing Meet Undergraduate Environmental Studies and Science.
Bibliographic record
Abstract
A Note on TerminologySince this dissertation addresses the interaction between different knowledges, cultures and peoples, I have adopted several conventions for terminology, although in practice, categories overlap, boundaries shift and change, and people move themselves (or are moved) in and out.Where possible, I have referred to Indigenous people by the name they use for themselves (such as Potawatomi, Haudenosaunee, Mi'kmaq), giving this affiliation in parentheses after their name.Each people has a different history and different values, traditions, ceremonies, and language, though some experiences and values exist in common.This study honors individual tribal differences as much as possible, while at the same time addressing an educational issue common to people of many different tribal affiliations.When referring to several peoples at once, I generally use "Native American" or "American Indian" for a U.S. context, "First Nations" or "Aboriginal" for a Canadian context, and "Indigenous" for a broader U.S./Canadian or international context.Even so, this array of terms leaves out names associated with important North American Indigenous peoples-Alaska Natives, Métis, Native Hawaiians, and others.I make my apologies here.I have used the descriptor "mainstream" or "Western" to refer to knowledge conventionally present in American and Canadian higher education courses and programs.Since environment-related education encompasses not only science, but also the humanities, social sciences, and arts, I focused on both environmental studies and science.* Including Native American, American Indian, Aboriginal, First Nations, and other Indigenous cultures of North America.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 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".