Intuition and animism as bridging concepts to Indigenous knowledges in environmental decision-making
Bibliographic record
Abstract
This study reports on student responses to ENVS 811: Multiple ways of knowing in environmental decision-making, a graduate level course which focuses on helping students come to some understandings of the connections between their own knowing and Indigenous ways of coming to know as they prepare for both professional and research careers in the environmental field.Although the inclusion of Indigenous knowledges into resource management decision-making processes is increasingly being recognized as important, effective application remains elusive.Lack of understanding, or acceptance, of the broad scope of Indigenous knowledges continues to make it difficult, if not impossible, for those trained in Eurocentric, or Western educational programs to include anything more than empirical observations by Indigenous peoples into environmental decision-making.In the context of this course, intuition and animism are used as useful bridging concepts to enable a fuller understanding, and valuing, of multiple ways of coming to know.Based on in-person interviews and responses to a brief email questionnaire, six key themes emerged.These included concerns about the role of the course within the larger program; connection to students' personal research and professional practice; and the impacts of: the particular course instructor, guest speakers, assigned course readings, and the structure of the course and individual classes.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".