Dancing with wolves: Making legal territory in a more-than-human world
Bibliographic record
Abstract
As human codings of animals are often simultaneously legal and spatial, it may be useful to bring together the animal geographies literature and scholarship on legal geography. Through a case study set in southwest Finland, we explore the emergent and fraught entanglements of wolves, humans and sheep, characterizing the attempts at the regulation of the wolf as entailing tense biopolitical calculations between the contradictory legal imperatives of biodiversity and biosecurity. Under the former, the wolf must be made to live; under the latter, it may need to die. These are worked out in and productive of two territorial configurations: the everyday spaces of encounter (real or imagined) between wolf and human, and the propertied territories of sheep farming. While human imperatives and anxieties are clearly central to these spatializations, we also seek to give the wolf its due, noting its important role in the making of legal territories. The coproduction of law and space, we conclude, offers important ethical lessons for humans in their relations to the wolf, as well as directing us to the need for more capacious thinking regarding territory.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".