Earth Science for all? The economic barrier to Geoscience conferences
Bibliographic record
Abstract
Abstract. Scientific meetings are vital for research development and networking. However, these events often perpetuate unconscious biases and barriers to diversity, particularly affecting ethnic minorities. The future success of geosciences depends on diversity, which enhances problem-solving and innovation through varied perspectives. This study examines the attendance diversity at the European Geosciences Union (EGU) General Assembly from 2005 to 2024, focusing on the impact of economic factors, distance, and population size on participation. Using publicly available data from the World Bank and EGU, this study finds that gross national income (GNI) is the primary determinant of attendance, with a strong correlation between GNI and participation, especially post-COVID. Distance also influences attendance but to a lesser extent, while population size shows a weak correlation. To improve diversity in academic conferences, we suggest facilitating donations, offering affordable accommodations, establishing additional travel funds, and rotating the conference location. Our actions must go beyond the EGU General Assembly and other geoscience conferences, extending to barriers to inclusivity within our community. By addressing these financial and systemic barriers, geoscience conferences can become more inclusive, benefiting the entire scientific community.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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".