Bibliographic record
Abstract
Besides the fact that it is located in the Western Hemisphere, belongs to island states of the Caribbean, and Fidel Castro has been its undisputed leader for a long time, what do we know about Cuba? In your opinion, the indigenous people of the island are surely black people, and only few know that Cuba is home to more than 100 thousand Chinese people, who came to the country many years ago to develop nickel reserves. At the same time, it turns out that 65 % of the Cuban population is white-skinned, and the problem of aging on the island is as topical as in Japan. With a more detailed study of life in this country, it turns out that Cuba is ahead of Brazil in terms of its development, occupies 33rd place according to its life expectancy (ahead of the United States, China and the United Arab Emirates), and has the lowest infant mortality rates in the Western Hemisphere after Canada. One of the most honorable professions in Cuba is the specialty of a physician, and, in total, about 70 thousand specialists with higher medical education work in the country with 11 million population. On the whole, this country has a rich history, and the history of health care development in Cuba is very interesting and informative.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".