Bibliographic record
Abstract
Juan Martinez picks fresh blueberries. With seven years of schooling and five years of experience, 25-year-old Juan can pick 50 to 75 pounds an hour, depending on yields. In southern Jalisco, Juan earns three times Mexico’s minimum wage of 173 pesos ($9) a day in 2022. Fresh blueberries are an agricultural success story. Consumption of fresh blueberries is rising due to their health benefits, convenient packaging, and year-round availability. Mexico was a latecomer to blueberry production, and benefitted from the development of varieties that thrive in warmer climates. Most Mexican blueberries are produced under plastic-covered hoop structures that protect the berries from birds and weather and reduce weed and pest pressures. Blueberries are harvested almost year-round in North America, so Juan could follow the sun and harvest blueberries all year. But Juan picks blueberries in Jalisco only from January through April, and then returns to his small farm in Chiapas. As Juan departs for southern Mexico, other Mexican workers are packing their bags to travel to Canada and the United States as legal guest workers, where they will be paid a piece rate wage of $0.50 a pound to pick blueberries and earn $15 to $25 an hour.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.685 | 0.333 |
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".