Bibliographic record
Abstract
The poorer countries of the world continue to struggle with an enormous health burden from diseases that we have long had the capacity to eliminate. Similarly, the health systems of some countries, rich and poor alike, are fragmented and inefficient, leaving many population groups underserved and often without health care access entirely. Cuba represents an important alternative example where modest infrastructure investments combined with a well-developed public health strategy have generated health status measures comparable with those of industrialized countries. Areas of success include control of infectious diseases, reduction in infant mortality, establishment of a research and biotechnology industry, and progress in control of chronic diseases, among others. If the Cuban experience were generalized to other poor and middle-income countries human health would be transformed. Given current political alignments, however, the major public health advances in Cuba, and the underlying strategy that has guided its health gains, have been systematically ignored. Scientists make claims to objectivity and empiricism that are often used to support an argument that they make unique contributions to social welfare. To justify those claims in the arena of international health, an open discussion should take place on the potential lessons to be learned from the Cuban experience. Keywords Cuba, public health, developing countries, international aid What is up with Cuba? Cuba remains an enigma to North Americans and Europeans alike. Two generations ago there was no society with the exception of Canada that was more tightly integrated into the US cultural and economic sphere. 1–3 After the revolution of 1959, however, Cuba acquired the pariah status of a wayward child and has been variously vilified in rhetoric, attacked militarily and economically, and consigned to cultural oblivion. Within the US academic community, Cuban dialogue has been maintained primarily by social scientists and historians, many of whom are second-generation Cubans.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".