Pharmacology, Toxicology, and Pharmaceutics Research Output in One Hundred and Fifty Countries for the Year 2019-2020
Bibliographic record
Abstract
In this note, the data about “Pharmacology, Toxicology, and Pharmaceutics” is compiled.Scopus has included pharmacology, toxicology & pharmaceutics (all), pharmacology,toxicology, pharmaceutics (miscellaneous), drug discovery, pharmaceutical science,pharmacology, and toxicology in the stated category. We analyzed the publications data ofone hundred and fifty (150) countries for 2019-2020. The data were independently screened,sorted, and extracted on 29th Dec 2020 (from Scopus). Based on the number of publications(NoP) and growth rate (GR), we designed and ranked the top country in each “publicationclub,” as shown in Table 1. Furthermore, if we ignore the minimum number of publications,the top ten ranked countries with growth rate are Uzbekistan (n = 1388.37), Ethiopia (n =238.64), Brunei Darussalam (n = 200.00), Gambia (n = 200.00), Mongolia (n = 171.43),Honduras (n = 150.00), Philippines (n = 144.00), Rwanda (n = 142.86), French Polynesia (n= 125.00) and Benin (n = 120.00). Based on the total publication record from more than 150countries (n = 118706 for 2020 and n = 100366 for 2019), a significant and positive growthrate (n = 18.27) has been noticed in “Pharmacology, Toxicology, and Pharmaceutics”. TheNoP and GR details of each country are provided in Table 2
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.028 | 0.066 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".