Bibliographic record
Abstract
Documenting Louisiana Sugar provides historians and social scientists with an innovative tool for examining plantation economy and agrarian society in the American South. Utilizing exceptionally detailed annual crop returns and additional census records, Documenting Louisiana Sugar makes available two fully searchable databases that allow users to examine in micro and macro detail the evolution of one of America's definitive plantation crops, namely cane sugar. These are available at the Download Center on this website. For over seventy years, agrarian economists in Louisiana diligently recorded economic and production data on each sugar producing estate. These remarkable records provide an unbroken time series of data; indeed, no other plantation crop in the American South was so meticulously recorded for such a long period of time as was Louisiana sugar. This project makes these sources available for rigorous analysis and provides users with the query functions capable of tracing people and plantations through time. It enables users to study the economic performance of an entire industry, to consider business consolidation, capital acquisition, technology transfer, and the shifting dynamics of plantation land use. The built in search functions enable researchers to limit or expand their enquiries by year, parish, crop output, technology, and even gender. Users can track persistence and change among the plantation elite, trace landholding and economic performance among both large and small cane farmers, examine the effect of the American Civil War, and assess the transition from slave to free labor on Louisiana's plantation economy. And for those interested in the late nineteenth century, the databases track the rise and fall of American sugar during U.S. imperial expansion. Former slave and abolitionist Frederick Douglass described Louisiana's sugar country as a life of living death. These databases do not tell the story of the hundreds of thousands of men and women who labored in the cane fields through the nineteenth century, but they tell the story of an industry where the exploitation of land, capital, and labor was central to business success. Funding for this project was made available by research project grants awarded by The Arts and Humanities Research Council of the United Kingdom, The Social Science and Humanities Research Council of Canada, and by the University of Sussex and the University of Toronto.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".