Bibliographic record
Abstract
Sugar drove the expansion of European empires in the Atlantic world. From its cultivation in the Atlantic Islands in the 15th century to its production in Cuba and Louisiana after British and French emancipation in the 19th century, sugar was always the dominant crop in the Atlantic. Wherever sugar was grown, the crop brought with it the same significant transformations, including a majority population of enslaved peoples of African descent, higher rates of mortality, lower rates of fertility, the concentration of capital on large plantations, and sweeping ecological changes such as the elimination of timber and the erosion of soils. Sugar profits made the circum-Caribbean world, in particular, a site of intense imperial rivalry. The sugar-growing regions of the Americas always imported more African slaves than did any other regions in the Americas. Cultivating sugar was deadly work. The decline of the slave population was the norm in the sugar-producing regions of the Americas. It was also a particularly lucrative crop. Sugar planters were among the wealthiest producers in the New World. The 19th-century abolitions of the slave trade destroyed the sugar industry in the Atlantic world by choking the industry’s labor supply. After abolition, East Indian and Chinese laborers were imported to try to sustain the sugar industry; but without enslaved African labor, it was no longer lucrative enough to compete with beet sugar production. The consumption of sugar expanded rapidly throughout the early modern era. The escalating demand drove the expansion of the sugar-producing regions. The sugar producers of the Caribbean struggled to find sufficient labor in the era of abolition and emancipation and shifted to various forms of coerced labor to continue producing the crop. This involved an ethnic shift as well: African slaves were replaced by Asian indentured laborers. The abolition of slavery and the rise of beet sugar finally halted the expansion of the sugar plantation complex in the Atlantic. This bibliography will address some of the major works on sugar in the Atlantic world. It will examine both the production and consumption of sugar and examine some of the most significant debates in the historiography on sugar slavery. It will contrast and compare sugar production and consumption by the various national and imperial groups, but it will focus largely on Anglo-American sugar production, reflecting a bias in the scholarly literature.
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 | 0.001 | 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 | 0.001 | 0.000 |
| 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 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".