Pharmacology, Toxicology, and Pharmaceutics Research Output in One Hundred and Fifty Countries for the Year 2019-2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it