Making sense of dictatorships and health outcomes
Bibliographic record
Abstract
Cuba is an authoritarian state and is poor even by the standards of Latin America. Yet it has managed to achieve levels of life expectancy and infant mortality that (even after adjusting for possible data manipulation)1–3 surpass those observed in advanced economies.4–8 Cuba is not the sole non-democratic regime to have achieved similar outcomes. The former Union of Soviet Socialist Republics (USSR) also stands as a clear example of such a case where there was a rapid increase in health outcomes post-1945, which made the USSR compare favourably with Western Europe9–11 in spite of the fact that it was relatively poorer.12 While it is true that, on average, dictatorships do not seem to improve health outcomes,13 14 Cuba, the USSR, and past or current autocratic regimes in China (especially in the recent outbreak of covid-19)15 or Ethiopia16 are well-known exceptions that are often praised. This is in part due to their impressive accomplishments in spite of low levels of economic development, as argued, in the case of Cuba, by Wenham and Kittelsen8 (pp11–12, 14–15) in this edition of BMJ Global Health . Numerous policy experts and policy-makers have recommended attempting to import the ‘good’ from such regimes (ie, high-quality, cheap healthcare) and leaving behind the ‘bad’ (ie, the non-democratic institutions, repressed private sector economy, the limited respect for human rights and other restrictions imposed by the regime).4–8 17 In this editorial, we point out that such a sorting of the wheat from the chaff is impossible. First, we point out that it is unsurprising to see some dictatorships performing well with regard to health indicators due to their ability to forcibly mandate the allocation of resources towards achieving the regime’s objectives. Second, we point out that there are trade-offs associated with …
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".