The EDIG project: a grassroots initiative working to address systemic inequities in geoscience on a global scale
Bibliographic record
Abstract
In early 2020, a group of geoscientists and other experts came together, within the framework of the Irish Centre for Research in Applied Geoscience (iCRAG), to learn about the challenges experienced by researchers in iCRAG, and to identify ways to work together to create a more inclusive environment. However, it was swiftly realised that these issues were manifest across the geosciences, and that any meaningful changes would need to be structural and widespread. This led to the formation of the Equity, Diversity and Inclusion in Geoscience (EDIG) project: a volunteer-led, virtual initiative, aiming to make geoscience more inclusive, accessible, and equitable. The EDIG project strives to improve awareness of the impact of prejudice, bias, exclusion, discrimination and other experiences within the larger geoscience community and to create strategies and networks to tackle inequities within geoscience.To help us better understand the challenges faced across the geoscience community, we ran an anonymous survey asking people about their experiences (or lack of) with equality, diversity, and inclusion related topics. The results of the survey helped to structure an online, free conference run over three days in December 2020. This inaugural event aimed to amplify the voices and experiences of underrepresented groups in geoscience in regard to equity, diversity and inclusion, drawing on the knowledge of 17 speakers from geoscience communities around the world.From the conversations at the 2020 event, we decided to expand outwards, opening our committee up to new volunteers and developing new projects to address barriers and challenges holistically. Many of these projects have involved collaborations with other initiatives and groups, including focused workshops (e.g., early career researcher barriers in Ireland) and are leading to new resources to help reach a wider network. In November 2022, we ran our second virtual conference, which sought to shift the conversation beyond increasing awareness toward strategies for action, and along with our original focus on improving awareness included sessions on data (collection, use, challenges) and how we might influence the future of equity, diversity and inclusion in geoscience.Going forward, our focus is to grow our network by building greater international links with other like-minded organisations (we’ve discovered that many people want to be involved, which is great!). We want to create a platform for us all to come together to work towards a more equitable and just geoscientific community. We also aim to raise awareness of the vital contributions of minoritized groups to geoscientific knowledge and the damaging consequences of their marginalisation and oppression in the history of our science. Only by creating a global network of supporters and activists can we hope to improve the diversity and inclusivity of our science. Let’s all come together to listen, learn and move forward together.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".