Scientific publishing in different countries: what simple numbers do not tell
Bibliographic record
Abstract
Background: The evaluation of scientific productivity is a well-established approach for assessing the quality of scientific activity of a single scientist, of a team of scientists, as well as of a university or a country. Methods: In this article, we aim to provide an update analysis of scientific publishing of the eight countries, seven of which belonging to the so-called “G7” (i.e., Canada, France, Germany, Italy, Japan, the UK and the US) plus China. The scientific output has then been normalized for the number of inhabitants and for the gross domestic product (GDP). Results: For the total number of publications, the US occupies the first position in the ranking, followed by China and UK. When the national scientific production is reported as number of publications for inhabitants, the UK and Canada are at the top of the ranking. Finally, when the national scientific production is reported in terms of number of publications for GDP, China is at the first place followed by the US. Conclusions: This analysis shows that the use of the total number of publications as the only index for assessing the quality of the scientific production of a single country may be misleading.
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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 | 0.026 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.019 | 0.039 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.015 | 0.035 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".