Geographical representation at EGU General Assemblies in the period of 2015-2019
Bibliographic record
Abstract
The European Geosciences Union (EGU) is the leading organisation for Earth, planetary and space science research in Europe. Each year the EGU holds a General Assembly that is the largest and most prominent European geosciences event, attracting over 16,000 scientists from all over the world. This presentation provides an overview of the geographical representation of participants to the EGU General Assembly in recent years. The presentation focuses on a five-year dataset spanning 2015 to 2019 and provides an insight on growth rates of the different countries individually and in comparison to the EGU General Assembly average growth (38% during the period 2015-2019). China has the fastest-growing representation at the EGU General Assembly with a growth rate close to 300% in the period 2015-2019. The growth rates of the Republic of Korea and Canada have also climbed, and now represent the second and third fastest-growing countries attending the EGU respectively, with growth rates just over 80%. The representation of Central and Eastern European countries (also known as EU-13 countries) at the EGU General Assembly has also grown steadily at a rate comparable with the EGU average, i.e. around 38%. Western European countries are the most represented at the annual general assembly accounting on average for 58% of the total participants over the 2015-2019 period. In addition the participation of Western Europeans to the general assembly continues to grow at the EGU but a slightly slower pace 29% than for Eastern Europe, but at a rate similar to participants from the USA. This analysis leads to the conclusion that participation at the EGU General Assembly has grown both in the total number of attendees and in their geographical diversity. The most striking shift in the representation of countries has been towards an increase in the participation of Asian countries (China, Taiwan, Rep. of Korea & Japan) that collectively now exceeds the participation of North American participants (USA + Canada). In particular, if the current rate of growth in participation is sustained by China over the coming years this dataset suggests that their representation will surpass that of the USA shortly. It was also clear that the EU-13 countries continue to participate in the EGU General Assembly in growing numbers and with particular representation in certain scientific divisions such as Soil System Sciences (SSS), Hydrological Sciences (HS) and Climate: Past, Present & Future (CL). Overall, the above data provide valuable guidance in how to shape future EGU actions to promote diversity, equality and inclusivity at the annual EGU meeting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".