In the (bleary) eye of the tiger: An anthropological journey into jungle backyards
Bibliographic record
Abstract
North America shelters a growing population of so-called ‘exotic animals’. If the phenomenon is not recent, it now fuels a considerable black market. Jungle backyards compose a non-negligible (yet often neglected) part of some modern ecological landscapes. This article explores problematical situations emerging from these shared humanimal lives. It presents the first results of a multi-species ethnography and examines the prevalence of what I call beastness – an antique commerce amid humans and animals that reveals not only utilitarian purposes, but also relational entanglements. Such a commerce feeds a sizeable economy and exerts major selective pressures (both biological and cultural) on organisms and their environment. For instance, there are more captive tigers living in the state of Texas alone than wild specimens running free anywhere else on the planet. From a strictly statistical point of view, the average tiger is no longer the tiger we imagine. Not wild anymore but neither quite domesticated, some animals – pioneers, in a sense – shuffle traditional taxonomical and ontological conceptions. Through biographical material, I reflect on adaptive responses as well as on zoological potentialities developed by this always-evolving bestiary. Providing serious case studies to further debates dealing with bio–eco–conservation, I discuss the influence of informational and communicational processes crystallized by some of our contemporary crossed becomings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.030 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".