Bibliographic record
Abstract
Abstract Coffee has played complex and diverse roles in shaping livelihoods and landscapes in Latin America. This tropical understory tree has been profitably cultivated on large estates, on peasant smallholdings, and at many scales in between. Coffee exports have fueled the economies of many parts of Latin America. At first, coffee farmers cleared and burned tropical forests to make way for their farms and increase production. Early farms benefited from the humus accumulated over centuries. In Brazil, farmers treated these tropical soils as nonrenewable resources and abandoned their farms once the soils were exhausted. In smaller coffee farms along the Cordillera—from Peru up to Mexico—coffee farming was not quite as wasteful of forests and soils. In the mid-20th century, scientific innovation in coffee farming became more widespread, especially in established coffee zones that were struggling with decreasing soil fertility, increasing soil erosion, and new diseases and pests. In the 1970s, national and international organizations promoted large-scale programs to “renovate” coffee production. These programs sought to dramatically increase productivity on coffee farms by eliminating shade, cultivating high-yielding coffee cultivars, and using chemical fertilizers and pesticides. Renovation brought tremendous gains in productivity over the short term, but at the cost of added economic and environmental vulnerability over the longer term. Since the end of the International Coffee Agreement in 1989, the global coffee market has become much more volatile. New coffee pioneer fronts are opening up in Brazil, Peru, and Honduras, while elsewhere coffee production is shrinking. NGOs and coffee farmers have promoted new forms of coffee production, especially Fair Trade and certified organic coffee. Still, most coffee farms in Latin America remain “conventional” farms, using a hybrid of modern and traditional tools. Economic and environmental sustainability remain elusive goals for many coffee farmers, and the threat is likely to increase as they grapple with the effects of climate change.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.006 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".