Between Corporatism and Socialism: Navigating the Waters of International Education in the Dominican Republic and Cuba1
Bibliographic record
Abstract
The Caribbean has long afforded U.S. and Canadian geographers with a rich venue of study-abroad opportunities. Physical and human geography classes are particularly well-suited to themyriad political, social, and environmental landscapes of the region. This article summarizes a few key experiences that have emerged in forging study-abroad venues in the Dominican Republic and Cuba. In the latter, the norms of operation have always been nebulous. Avoiding criticisms of the Castro government is paramount, accommodating tour-guide spies is often necessary, and getting away from the top-down party line is essential. In the former, we describe a study-abroad site that is, culturally at least, so similar to eastern Cuba that it is uncanny. However, the low presence of public institutions and the prevalence of strong corporate agents impose another set of obstacles that must be carefully traversed. The case studies illustrate how geographic field research cannot be divorced from broader political, economic, and corporate interests and that how geographers grapple with these matters should not be done out of the students ’ view. Rather, with the use of discretion, these obstacles can provide valuable “teachingmoments ” that are the backbone of international education.
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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 | 0.002 | 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 | 0.020 | 0.021 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".