More-than-human histories and the failure of grand state schemes: sylviculture in the New Forest, England
Bibliographic record
Abstract
As James Scott’s Seeing Like a State attests, forests played a central role in the rise of the modern state, specifically as test spaces for evolving methods of managing state resources at a distance, and as the location for grand state schemes. Together, such ambitions necessitated both the elimination of local understandings of forest management — to be replaced by centrally controlled scientific precision — and a narrowing of state vision. Forests thus began to be conflated with trees (and their timber) alone. All other aspects of the forest, both human and non-human, were ignored. Through the lens of the 18th and early 19th century New Forest in southern England, this paper examines the impact of government attempts to shift the focus of state forests from being remnant medieval hunting spaces to spaces of income generation through the creation of vast sylvicultural plantations. This state scheme not only reworked the relationship between the metropole and the provinces — something effected through systematic surveys and novel bureaucratic procedures — but also dramatically impacted upon the biophysical and cultural geographies of the forest. By equating forest space with trees alone, the British state failed to legislate for the actions of both local commoners and non-human others in resisting their schemes. Indeed, subsequent oppositions proved not only the tenacity of commoners in protecting their livelihoods but also the destructive power of non-human actants, specifically rabbits and mice. The paper concludes that grand state schemes necessarily fail due to their own internal illogic: the narrowing of state vision creates blind spots in which human and non-human lives assert their own visions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".