Dene Understanding of the Land: On Habitats and Relationships, and Reflections on Change
Bibliographic record
Abstract
I attended the Society of Ethnobiology 31 Annual Conference in Fayetteville Arkansas April 16-20, where I presented a paper in the symposium on Native North American Ethnobiology on the afternoon of April 17. The session was quite diverse, and had three Native American presenters among the participants, including a former MA student of mine from the MAIS program, Zoe Dalton, now at the University of Toronto doing her PhD in Geography. . The paper was well received, and I received positive comments from several of the Cherokee attendees as well as from colleagues, who also offered helpful suggestions for working further with the ideas presented, including suggestions to present the analysis in terms of cultural keystone species, and also of source material from NOAA on environmental change and Inuit traditional knowledge. Other highlights of the conference were: The Native Scholars’ Symposium: Indigenous Ethnobiology, a panel discussion featuring a series of Cherokee artists, and academics with whom they had collaborated in various ways. This discussion explored some of the positive, and potentially problematic aspects of collaboration between indigenous peoples and (non-indigenous) academics. The Cherokee art show and address by Dr. Nancy Maryboy, Navajo/Cherokee astronomer, on indigenous and western sciences and indigenous education at the Conference Banquet. The field trip to the Arkansas wine growing district, where we learned about how German viticulture traditions were brought to Arkansas in the 1870’s, and how grape growing and wine making incorporated hybridization of Viutis vinifera with two native North American grape species. We toured the vineyard, and the working part of the winery, and learned something of the grape growing industry and its health food aspects. I also had an opportunity to meet with four of the contributors to my co-edited volume Landscape Ethnoecology, which is being revised after review for submission to Berghahn in May. Attending these meetings also facilitates networking with colleagues from all over the world and keeping abreast of current developments in Ethnobiology. Attendees included colleagues from Australia, Nepal and Kenya, as well as Americans, Canadians, and colleagues who had travelled from Latin 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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.076 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".