Bibliographic record
Abstract
Abstract “The empire is coffee. And coffee is the valley.” This common phrase for a long time dominated the Brazilian imaginary about coffee, but it doesn’t translate the truth of the Brazilian economy of the 19th century. Coffee, the “black gold,” was Brazil’s main export product in the 19th century, and its main producing region was the Paraíba do Sul River Valley, which encompassed the provinces of São Paulo (high Paraíba) and Rio de Janeiro (middle and lower Paraíba). But the economy of the Brazilian empire cannot be reduced to coffee plantations. In other Brazilian regions, there were other primary products such as livestock products, resources extracted from the Amazon rainforest, and others. Minas Gerais, the largest Brazilian slave province, was not a producer region for export. In addition, there was a transformation in the "secondary" sector, with handicrafts, factories (sets of workshops), and manufacturing, both in the city and in the countryside, with slave and free labor. The political stability and economic growth of the mid-19th century made Brazil a region of foreign direct investment (FDI), mainly British, in sectors such as infrastructure (railways and ports), banks, insurance companies, and industry. In the last quarter of the 19th century, modern textile industries emerged, mainly in the Center-South, alongside the expansion of coffee in São Paulo, Minas Gerais, and Rio de Janeiro (Zona da Mata Mineira).
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".