Undoing Nature: The John Muir Trust's “Journey for the Wild”, the UK, Summer 2006
Bibliographic record
Abstract
Abstract: I take as a point of departure for a discussion of the idea of nature the John Muir Trust's much publicised Journey for the Wild which took place in the UK during the summer of 2006. My objective is to explore how, at the same time that the “wild” was performed as a political category through the Journey, replicating the binary nature/society, prevalent norms of nature that depend on that binary, including, ironically, those of John Muir himself, were “undone”. I work with Judith Butler's (2004, Undoing Gender) ideas of “doing” and “undoing” gender and what counts as human, and her link between the articulation of gender and the human on the one hand and, on the other, a politics of new possibilities. Taking her argument “elsewhere”—unravelling what is performed as “wild” and what counts as “nature”—and using as evidence the art of Eoin Cox, the actions of journeyers, extracts from their diaries and from Messages for the Wild delivered to the Scottish Parliament, I suggest that the idea of a working wild points towards more socially just political possibilities than a politics of nature defined through a binary.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".