United Kingdom’s contribution to European research output in biomedical sciences: 2008–2017
Bibliographic record
Abstract
Background : On 31 January 2020, the United Kingdom (UK) formally left the European Union (EU). Only a short transition period, until 31 December 2020, is available to negotiate collaborations for research in biomedical sciences and health care. Within the European scientific community, two opinions are common: 1) Brexit is an opportunity to obtain more funding at the expense of the departing British; and 2) UK colleagues should continue to collaborate in EU scientific efforts, including Horizon Europe and Erasmus+. To provide evidence for more informed negotiations, we sought to determine the contribution of the UK to EU&rsquo;s research in biomedical sciences. Methods : We performed a macro level scientometric analysis to estimate the contribution of the UK and EU member states, including those associated with EU-funding (EU+) namely Albania, Armenia, Bosnia-Herzegovina, Faroe Islands, Georgia, Iceland, Israel, Macedonia, Moldova, Montenegro, Norway, Serbia, Switzerland, Tunisia, Turkey, and Ukraine, to preclinical, clinical and health sciences. We searched the Web of Science database to count the total number of scientific publications and the top 1% most cited publications in the world between 2008 and 2017, calculated the performance efficiency by dividing the top 1% by the total number, and calculated the odds ratios to create a ranking of performance efficiency. We then compared the contribution of the UK to all the EU+ -based publications and the top 1% to the contributions of the ten EU member states with the largest biomedical research output and also compared the respective contributions to EU+ publications that resulted from collaborations with other regions in the world. Results : We found 2,991,016 biomedical publications from EU+ during 2008&ndash;2017, of which 19,019 (0.64%) were in the world&rsquo;s top 1% of the most cited publications. The UK produced 665,467 (22.3%) of these publications and had over two and a half times more top 1% most cited publications than the EU+ (odds ratio 2.79, 95% CI 2.71&ndash;2.88, p < 0.001). The UK&rsquo;s share in the EU+ co-publications with regions outside Europe ranged between 23.0% for the Arab League and 50.6% for Australia and New Zealand and its share of the top 1% ranged between 48.6% for the USA and Canada and 70.7% for the African Union. Conclusions : The UK contributed far more highly cited publications than the rest of the EU+ states and strongly contributed to European collaborations with the rest of the world during 2008&ndash;2017. This suggests that if the UK ceases to participate in EU scientific collaborations as a result of Brexit, the quantity and quality of EU&rsquo;s research in biomedical sciences will be adversely affected.
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How this classification was reachedexpand
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 | 0.056 | 0.388 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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; both teacher heads agree on what is shown here.
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".