Poached lives, traded forms: Engaging with animal trafficking around the globe
Bibliographic record
Abstract
From the depths of the Borneo jungle to private ménageries through the dark web, this article investigates the expansion of contemporary wildlife trafficking and maps an early twenty-first-century booming trade in living organisms, dead animal parts and metempsychic imaginaries. Fuelled by a multiplicity of emergent relational entanglements, such traffic involves life and death matters, big money interests, coveted commercial routes (and their extensive influence over land, people and spirits) as well as deep affective states infused with apocalyptic narratives, blood and bullets, tourism and terrorism. Here I concentrate on the curious case of pangolin poaching and identify problems pertaining to the characterization of life forms when such forms are massively poached, extensively traded and, overall, continuously transfigured along various registers of activities. Concomitantly, I detect in today’s so-called ‘multispecies-turn’ a problematic conceptualization of what an animal (individual or species) is – be this animal alive or dead, whether it should be hunted, protected, consumed, reproduced, mourned, or even held responsible for a new geological epoch. Rather than assuming the given of an already individuated form (from which to consider either pre-conceived or post-confirmed developmental stages), I draw on individuating processes that actually enable individuals to emerge (and emergence to individuate). While distinguishing between dynamics of concrescence and indetermination, I offer positive, operative and alternative concepts to re-engage with mo(ve)ment of shared becomings. Here, the animal is approached as an event.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".