Honduras publications in the Science Citation Index Expanded: institutions, fields and authors
Bibliographic record
Abstract
"Honduras is the second largest country in Central America, but 63 % of its population lives in poverty and it is the Central American country with less scientific journals. Even though Honduras has been included in general studies about Latin American science, there are no specific bibliometric studies about the productivity of the country, so this is the first formal study about the most productive institutions, fields and authors in Honduras. The Science Citation Index Expanded (SCI-EXPANDED), Web of Science Core Collection was used to collect the bibliographic data. There are no Honduras publications from 1903 to 1972 in SCI-EXPANDED. Honduras publications from 1973 to 2015 were further analyzed. A total of 1 146 Honduras publications with 13 document types in the Science Citation Index Expanded from 1973 to 2015 were found. Nearly 95 % of the articles in the database are in English, suggesting that articles in this language have the greatest visibility in the database, similar to other Central American countries. The countries with which Honduras publishes (e.g. Mexico, other Central American countries) follow the geographic and cultural affinity model, i.e. researchers tend to collaborate with colleagues that have similar culture or that are geographically close. This pattern has been found for other Central American countries. The focus of Honduran scientists in health and agriculture problems is typical on the less developed countries; on this respect Honduras is more similar to its closest neighbor, Nicaragua, than to smaller but more developed Central American countries like Panama and Costa Rica. Overall, the situation of scientific research and output in Honduras is improving, with more articles and citation in the SCI-EXPANDED, and this positive trend should bring about benefits for the people of Honduras. Rev. Biol. Trop. 65 (2): 657-668. Epub 2017 June 01."
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.070 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".