Bibliographic record
Abstract
The Mexican chocolate cluster is taking off. In 2009, it received 3 percent of all incoming foreign direct investment. It grew exports to the U.S. by 63 percent, solidifying its position as its second-largest chocolate supplier and stealing market share from #1 and #3 players, Canada and Belgium. In the same year, Hershey’s, which until recently produced in Mexico only for the local market, made it an important export platform. Major global processing company Barry Callebaut followed, locating their third largest cocoa processing plant next to Hershey’s plant in Monterrey, Mexico. The recent rush into Mexico can be attributed partly to the process of liberalization of trade between the U.S. and Mexico under NAFTA. Most importantly, a 2006 change in the rules of origin allowed duty-free trade of chocolate produced of non-NAFTA inputs. These changes, in addition to the availability of cheaper labor and lower-priced sugar in Mexico relative to the U.S. and Canada make it an attractive export platform. But can Mexico create a strong cluster and sustain a long-term competitive advantage as an exporter of chocolate? We believe that Mexico has many of the pre-conditions needed for cluster development, including a critical mass of existing companies, world-class multinational companies, a favorable location with unlimited access to the large and sophisticated U.S. demand, and some advantages in sugar.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".