Arboreal Methodologies: Getting Lost to Explore the Potential of the Non-innocence of Nature
Bibliographic record
Abstract
This paper recounts a workshop that took place in a polytunnel in a forest school in Sligo, North-West Ireland on a cold day in early-December. The event sought to materialize ‘arboreal methodologies’ (Osgood, 2019; Osgood & Odegard, 2022; Osgood & Axelsson, 2023) which are characterized by the enactment of feminist new materialist praxis to engage in world-making practices (Haraway, 2008) intended to unsettle recognizable tropes of biophilia that have come to frame both child and nature in narrow ways. The arboreal methodologies that participants were invited to mobilise were situated, material, affective, and involved metaphorical and material practices of ‘getting lost’. The workshop invited a sense of wonder at the ways arboreal methodologies might offer possibilities to confront human exceptionalism and wrestle with our complex, often contradictory relationships to ‘nature’. The approach taken involves methodologies without method (Koro-Ljunberg, 2016) to bring speculative, embodied encounters in the forest, together with unlikely tales of how forests work on and through us. We pursue a critical, tentacular engagement with the forest and take seriously its potential to agitate familiarity and strangeness, wonder and fear, nature and culture. In this paper we re-encounter embodied becomings-with the forest to think and sense other ways to take life in the Plantationocene (Tsing, 2015) 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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.074 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.004 | 0.009 |
| 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".