Life for Sale? The Politics of Lively Commodities
Bibliographic record
Abstract
When so many facets of nonhuman life are commodified daily with little challenge, this paper looks to shed light on what is objectionable about commodifying nonhuman life. As a contribution in this direction, we undertake a comparative examination of the formation of two different but equally lively, and international, commodities: Exotic pets and ecosystem carbon. In this paper we first set out to understand what characteristics of life matter in the production of the commodity. We argue that a particular mode of value-generating life predominates in each commodity circuit: in exotic pet trade, an individualized, ‘encounterable’ life; in ecosystem services, an aggregate, reproductive life. Second, we find that hierarchies between humans and other beings are highly generative in the formation and effects of lively commodities. On one hand, these hierarchies cast nonhumans in a disposable state that is integral to the functioning of exotic pet trade; on the other hand, these hierarchies are partly what ecosystem services are designed to address. Nevertheless, we find that reproduction of uneven species geographies is at work in both economies. The degree and nature of effect on the material conditions of nonhuman lives is, however, distinct, and our conclusion calls for greater attention to these differences.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".