Consider the Coconut: Scientific Agriculture and the Racialization of Risk in the American Colonial Philippines
Bibliographic record
Abstract
This article invokes the “molecular intimacies of empire” to illuminate the links between the superfood status of coconut oil and plantation labor in the American colonial Philippines. Prior to the American occupation in 1898, coconuts were a local crop that offered small growers a degree of protection from capitalist agriculture. A mere two decades later, coconut plantations occupied more than two million acres of land; copra – the dried kernels from which oil is pressed – was the archipelago’s third major export industry; and the industry employed at least four million people along a commodity chain that included prisoners, landed planters, and oil refiners. Transimperial tropical research stations, economic botany, and penal farms propelled this change. US-run prison plantations in the southern Philippines served as living laboratories for the racial management of labor and the bioengineering of trees bearing fruit all year. Though the copra trade comprised production of modern extractive capitalism, American dairy farmers and vegetable oil producers racialized copra imports as a tropical threat to the white body politic during the global Great Depression. Yet this conflation of coconut oil and the imagined tropical primitive positioned coconut oil for its rerendering as an unrefined natural health food. By connecting the colonial planation to the coconut’s superfood status, the article shows how discourses of risk are racialized and consumed. Indeed, is not the body of the laborer who risks exposure to fertilizers and pesticides nor the loss of biodiversity that North American consumers consider when asked if coconuts are a health food.
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 | 0.002 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".