Milking economies: Multispecies entanglements in the infant formula industry
Bibliographic record
Abstract
In 2016 the Chinese infant formula company Feihe International signed a deal with the Canadian Dairy Commission (CDC) to process Canadian cows’ and goats’ milk for infant formula export to China. Our purpose in this paper is to understand how this deal – and the new Feihe formula factory located in Kingston, Canada – is underpinned by a series of multispecies entanglements across cow, human and goat mothers in China and Canada. To do so, we analyse official correspondence between the CDC, Feihe and City of Kingston; market reports for the dairy, goat and infant formula industries; and news articles about the Feihe infant formula plant. Conceptually, we develop an anti-colonial, multispecies entanglement framework to chart the violent inclusions, exclusions and typologizations that make milk and formula economies possible. We are specifically interested in how the Feihe–CDC deal (re)configures entanglements across species, nation, race, science and motherhood. To understand these relations, we heuristically imbricate two different sets of entanglements that underpin this deal: milk drinking, empire and genetic purity across race, breed and species; and motherhood, science and technology across humans, goats and cows. We use our threefold entanglement framework to better understand the violence of these imbrications and to work towards a multispecies feminist ethic in the infant formula industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".