Approaching Nonhuman Ontologies: Trees, Communication, and Qualitative Inquiry
Bibliographic record
Abstract
Consideration of trees has historically been confined to disciplinary, quantitative perspectives embedded in botany, earth sciences, resource management, environmental sustainability, and sustainable development wherein trees are largely viewed as senseless, bio-mechanical matter to be controlled and used for human consumption and economic gain. In this article, I reflect selectively on methodologies and methods I used in a broader, interdisciplinary project to study the sentient, intelligent relationality of trees as agentic, conscious, innovative entities embedded in unique, community-based lifeways. My research framework integrated Indigenous research methodologies, public ethnography, ontological emergence theory, plant science, philosophies of plant and nonhuman knowing, interspecies communication, and filmmaking. Herein, I focus on how perspectives and approaches based on qualitative, ethnographic inquiry and Indigenous epistemologies support and broaden research, (re)presentation, and engagement with trees and other nonhumans. Methods I discuss include the practices of cultivating tree/human communication and fostering human sensitivity and embodied knowing.
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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 | 0.063 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".