Tank to Table: Hong Kong’s Wet Markets and the Geographies of Lively Commodification Beyond Companionship
Bibliographic record
Abstract
We argue for a radical reconfiguration of existing theorizations of the “lively commodity”—beings captured, cultivated, and traded for their very lives—on more inclusive terms. Specifically, we advocate the inclusion of animals intended for dietary consumption, in recognition of the demonstrable centrality of encounters between human beings in their role as consumers and the animals and animal parts offered for sale in Hong Kong’s many wet markets to the processes of commodification. Based on semistructured interviews with vendors and consumers (n = 86) and a variety of modes of ethnographic observation (including narrative, photography, and several forms of videography), we analyze three groups of practices and strategies for structuring and negotiating productive encounters (which we label provoking motion, stimulating appetite, and maintaining life) observed in twenty-seven different wet markets across Hong Kong between June and September 2022. Our analysis also suggests critical issues and directions for future research rooted in, at minimum, crucial differences in the scalar, temporal, ecological, and ethical dimensions of diverse processes of lively commodification.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".