Listening to the Literal: Orientations Towards How Nature Communicates
Bibliographic record
Abstract
This paper begins with an assumption that the natural world is literally able to speak. What follows is research around a new place-based, ecological and imaginative public school in Maple Ridge, BC. The school has no building to speak of as there is an attempt being made, as part of the day-to-day pedagogical practice, to listen to the more-than-human as an active voice and co-teacher thereby moving from human teachers/researchers speaking in, about and for the more-than-human towards speaking with and listening to it. Drawing on our lived experience as researchers, theorists, and ecological educators, this paper proposes to draw on the student voices at the Environmental School to posit a series of five distinct orientations. Each of these orientations is potentially available to us and each offers a different way to understand, attend to and communicate with the natural world. These orientations have implications, if taken seriously, for educational practice and content. In this paper, we focus on clarifying these orientations and anchor them with examples from interviews done over the course of several school years with three different students. We end the paper by pointing out some of the educational implications that might arise if we are to take these students and, as a result, the proposed orientations seriously.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.077 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".